An open, large-scale, community-driven dataset of medical ultrasound raw channel data
+
The largest, openly licensed dataset of medical ultrasound channel capture data
-
+
-
-
-
-
-
-
+ℹ️ This repository is currently available in early access to project contributors.
## Dataset Description
-Open-H-RF is a community-driven dataset initiative building the open, shared foundation needed to train and evaluate AI models built on pre-beamformed (channel capture) medical ultrasound measurements.
+OpenH-RF is a community-driven dataset initiative building the open, shared foundation needed to train and evaluate AI models built on pre-beamformed (channel capture) medical ultrasound measurements.
-This dataset is a collection of RF samples and metadata in the [`zea` file format](https://github.com/tue-bmd/zea) from a variety of tasks and applications, including ultrasound localization microscopy, ultrasound computer tomography, b-mode, flow imaging and more. The dataset catalog and gallery is available at [open-h.github.io/Open-H-RF](https://open-h.github.io/Open-H-RF).
+This dataset is a collection of RF samples and metadata in the [`zea` file format](https://github.com/tue-bmd/zea) from a variety of tasks and applications, including ultrasound localization microscopy, ultrasound computer tomography, b-mode, flow imaging and more.
-Each subdirectory here holds a data card and a processing pipeline. The matching reconstruction scripts, one runnable reference reconstruction per subset, are in the companion repository, [github.com/open-h/Open-H-RF](https://github.com/open-h/Open-H-RF).
+Each subdirectory here holds a data card and a processing pipeline. The matching reconstruction scripts, one runnable reference reconstruction per subset, are in the companion repository, [github.com/open-h/OpenH-RF](https://github.com/open-h/OpenH-RF).
This dataset is ready for commercial or non-commercial uses.
-## Maintainer / Hosting Organization:
+## Dataset Owner
NVIDIA Corporation
@@ -38,7 +33,7 @@ NVIDIA, Stanford University, Eindhoven University of Technology, Tel Aviv Univer
## Dataset Stewardship
-Open-H-RF community & Steering Group
+OpenH-RF community & Steering Group
## Dataset Creation Date
@@ -80,15 +75,10 @@ Channel capture data and related meta data are saved in HDF5 data files using a
## Reference(s)
-- https://github.com/open-h/Open-H-RF
-- https://arxiv.org/abs/2610.13616
+https://github.com/open-h/OpenH-RF
## Ethical Considerations
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer teams to ensure this dataset meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
-
-## Citation
-
-If you use Open-H-RF in your research, please cite the [Open-H-RF paper](https://arxiv.org/abs/2610.13616). The BibTeX entry is in the [GitHub repository](https://github.com/open-h/Open-H-RF#citation).
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diff --git a/colorado-boulder/README.md b/colorado-boulder/README.md
index 09ae5730f94d00eab87c8639d41306e66d051302..42b5aebd8d9a694dc51e2946f7a0f4f6de560e67 100644
--- a/colorado-boulder/README.md
+++ b/colorado-boulder/README.md
@@ -7,7 +7,7 @@ task_categories:
tags:
- ultrasound
- rf
- - open-h-rf
+ - openh-rf
- 3d
language:
- en
@@ -20,7 +20,7 @@ size_categories:
| 2D ATS 539 phantom | 3D phantom | In-vivo quadriceps |
|:---:|:---:|:---:|
|  |  |  |
-| [`Sub-dataset-1`](https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/colorado-boulder/Sub-dataset-1) | [`Sub-dataset-2`](https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/colorado-boulder/Sub-dataset-2) | [`Sub-dataset-3`](https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/colorado-boulder/Sub-dataset-3) |
+| [`Sub-dataset-1`](https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/colorado-boulder/Sub-dataset-1) | [`Sub-dataset-2`](https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/colorado-boulder/Sub-dataset-2) | [`Sub-dataset-3`](https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/colorado-boulder/Sub-dataset-3) |
*Motion-compensated tracked SSA reconstructions, one per sub-dataset. Each frame beamforms the raw RF channel data with its own tracked probe pose; a 40 mm window of frames is then coherently summed to synthesise a larger effective aperture, and the window slides along the freehand sweep.*
@@ -30,7 +30,7 @@ This dataset contains tracked swept synthetic aperture (SSA) ultrasound acquisit
The data were acquired using a Verasonics Vantage research ultrasound system with a P4-2 phased array transducer. The dataset includes raw RF channel data, acquisition parameters, probe geometry, transmit information, and frame-wise tracked probe pose metadata.
-The purpose of this dataset is to provide reproducible examples for generalized ultrasound reconstruction, with a particular focus on motion-compensated tracked SSA reconstruction using the zea/Open-H-RF data format.
+The purpose of this dataset is to provide reproducible examples for generalized ultrasound reconstruction, with a particular focus on motion-compensated tracked SSA reconstruction using the zea/OpenH-RF data format.
The phantom acquisitions do not contain human subject data, animal data, or protected health information (PHI). The in-vivo acquisitions were collected from healthy volunteer participants and de-identified prior to release.
@@ -88,7 +88,7 @@ None (N/A).
## Processing the Dataset
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/colorado-boulder/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/colorado-boulder/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
The script selects tracked frames at roughly 1 mm lateral spacing and writes the reconstruction to `ssa_bmode.png`. Point `ZEA_FILE` at any acquisition in the corpus to reconstruct it. [`assets/main_bmode.png`](./assets/main_bmode.png) was produced this way, compounding the full sweep into one image; the loops at the top of this card slide a shorter aperture window along the sweep instead.
@@ -98,7 +98,7 @@ The script selects tracked frames at roughly 1 mm lateral spacing and writes the
[zea v0.1.7](https://github.com/tue-bmd/zea)
-The dataset is distributed in the zea/Open-H-RF HDF5 format.
+The dataset is distributed in the zea/OpenH-RF HDF5 format.
Each file includes:
diff --git a/concordia/README.md b/concordia/README.md
index ec7919ca8331e29cb6b104d4a3a2d8a59e3f88dc..e53b6e21470b9dc62f2d3313bd534df48ca9d16b 100644
--- a/concordia/README.md
+++ b/concordia/README.md
@@ -8,7 +8,7 @@ task_categories:
tags:
- ultrasound
- rf
- - open-h-rf
+ - openh-rf
- synthetic
- field-ii
language:
@@ -21,11 +21,11 @@ size_categories:
-*The five phantom classes (anechoic, hypoechoic, hyperechoic, diverse, point-target), one capture per column: [`image_0012`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/concordia/data/image_0012.hdf5), [`image_0410`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/concordia/data/image_0410.hdf5), [`image_0733`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/concordia/data/image_0733.hdf5), [`image_0753`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/concordia/data/image_0753.hdf5), [`image_1808`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/concordia/data/image_1808.hdf5). Rows show the reference B-mode (`data/image`), the simulated scatterer cloud (`data/scatterers`) and the label map (`data/segmentation` for the lesion classes, `data/diverse_source_image` for diverse; point-target captures store none).*
+*The five phantom classes (anechoic, hypoechoic, hyperechoic, diverse, point-target), one capture per column: [`image_0012`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/concordia/data/image_0012.hdf5), [`image_0410`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/concordia/data/image_0410.hdf5), [`image_0733`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/concordia/data/image_0733.hdf5), [`image_0753`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/concordia/data/image_0753.hdf5), [`image_1808`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/concordia/data/image_1808.hdf5). Rows show the reference B-mode (`data/image`), the simulated scatterer cloud (`data/scatterers`) and the label map (`data/segmentation` for the lesion classes, `data/diverse_source_image` for diverse; point-target captures store none).*
## Dataset Description
-SynthUS-FSA is a fully synthetic corpus of pre-beamformed full-synthetic-aperture (FSA) ultrasound channel-data captures, generated with the Field II simulator to model a 128-element L11-5v linear array. Each capture fires one transmit event per element (128 transmits, receive on all 128 elements), so every element-to-element combination is retained and any receive/transmit beamforming scheme (focused B-mode, multi-angle plane-wave compounding, diverging-wave, adaptive/compressive schemes) can be retrospectively synthesized from the same channel data. The dataset is packaged in [zea](https://zea.readthedocs.io) format — the Open-H-RF reference Python library for ultrasound file I/O and beamforming — which every `.hdf5` file and `reconstruct.py` depend on. The dataset targets three Open-H-RF task categories: (1) generalized reconstruction (FSA channel data paired with a synthesized full synthetic-aperture B-mode as ground truth), (2) compressed sensing / adaptive transmit (retrospective sub-selection of the 128 transmit events — derived from the same captures, no additional files), and (3) segmentation (binary echogenicity masks on a subset of phantoms). All data is simulated — no clinical, phantom-hardware, or animal acquisition is involved.
+SynthUS-FSA is a fully synthetic corpus of pre-beamformed full-synthetic-aperture (FSA) ultrasound channel-data captures, generated with the Field II simulator to model a 128-element L11-5v linear array. Each capture fires one transmit event per element (128 transmits, receive on all 128 elements), so every element-to-element combination is retained and any receive/transmit beamforming scheme (focused B-mode, multi-angle plane-wave compounding, diverging-wave, adaptive/compressive schemes) can be retrospectively synthesized from the same channel data. The dataset is packaged in [zea](https://zea.readthedocs.io) format — the OpenH-RF reference Python library for ultrasound file I/O and beamforming — which every `.hdf5` file and `reconstruct.py` depend on. The dataset targets three OpenH-RF task categories: (1) generalized reconstruction (FSA channel data paired with a synthesized full synthetic-aperture B-mode as ground truth), (2) compressed sensing / adaptive transmit (retrospective sub-selection of the 128 transmit events — derived from the same captures, no additional files), and (3) segmentation (binary echogenicity masks on a subset of phantoms). All data is simulated — no clinical, phantom-hardware, or animal acquisition is involved.
A similar data-generation approach was used in Sharifzadeh et al. (2024). Users of this dataset are kindly requested to cite that work:
@@ -82,13 +82,13 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
```bash
zea process \
- --dataset hf://nvidia/Open-H-RF/concordia/data/image_0005.hdf5 \
- --config hf://nvidia/Open-H-RF/concordia/pipeline.yaml \
+ --dataset hf://nvidia/OpenH-RF/concordia/data/image_0005.hdf5 \
+ --config hf://nvidia/OpenH-RF/concordia/pipeline.yaml \
--n-frames 1 \
--save-as png
```
-Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/concordia/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF).
+Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/concordia/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF).
## Dataset Format
@@ -107,7 +107,7 @@ Pre-processing applied before packaging: anti-alias FIR decimation (factor 5, 10
## Dataset Quantification
-**Current Open-H-RF release:** 2,000 HDF5 files; 82.39 GB (82,390,286,336 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 2,000 HDF5 files; 82.39 GB (82,390,286,336 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
This dataset comprises 2,000 FSA captures across five echogenicity classes:
diff --git a/dartmouth-uct/README.md b/dartmouth-uct/README.md
index 34052b4c731283a1675edc6a00f40181f624e8db..14630c11bc77e366313038970c72abf37c8620d9 100644
--- a/dartmouth-uct/README.md
+++ b/dartmouth-uct/README.md
@@ -7,7 +7,7 @@ task_categories:
tags:
- ultrasound
- rf
- - open-h-rf
+ - openh-rf
- usct
- sound-speed-estimation
- attenuation
@@ -23,7 +23,7 @@ size_categories:
-*A 2D-sim breast cross-section, [`data/2d/phantom_179604449_z200.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/dartmouth-uct/data/2d/phantom_179604449_z200.hdf5), reconstructed as a reflectivity image beside the ground-truth sound-speed and attenuation maps stored in the file.*
+*A 2D-sim breast cross-section, [`data/2d/phantom_179604449_z200.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/dartmouth-uct/data/2d/phantom_179604449_z200.hdf5), reconstructed as a reflectivity image beside the ground-truth sound-speed and attenuation maps stored in the file.*
## Dataset Description
@@ -96,7 +96,7 @@ Both collections are generated from the **same underlying digital breast phantom
## Processing the Dataset
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/dartmouth-uct/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/dartmouth-uct/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
Set `ZEA_FILE` at the top of the script and run `python reconstruct.py`; the figure at the top of this card is its output. `SOS_MAP = True` swaps the constant-sound-speed delays for a straight-ray integral through the ground-truth map.
@@ -115,7 +115,7 @@ All data are packaged in the **`zea` HDF5 file format** (one `.hdf5` file per ac
## Dataset Quantification
-**Current Open-H-RF release:** 2,625 HDF5 files; 257.62 GB (257,619,787,776 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 2,625 HDF5 files; 257.62 GB (257,619,787,776 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
- **Number of acquisitions:** **2,149 (2D-sim)** (1,859 dense + 290 fatty) + **476 (3D-sim)** = **2,625 acquisitions**.
- **Single-Tx channel-capture frames:** 2,149 × 256 (2D-sim) + 476 × 64 (3D-sim) ≈ **5.8 × 10⁵ frames**.
diff --git a/jhu-pulselab/README.md b/jhu-pulselab/README.md
deleted file mode 100644
index 5d3d142e674aa05de2b11b2ca0c226f52dd77279..0000000000000000000000000000000000000000
--- a/jhu-pulselab/README.md
+++ /dev/null
@@ -1,69 +0,0 @@
----
-name: jhu-pulselab
-pretty_name: "UltraSound ToolBox (USTB) Channel Capture Data, PULSE Lab, Johns Hopkins University"
-license: cc-by-4.0
-task_categories:
- - image-to-image
-language:
- - en
-tags:
- - ultrasound
- - rf
- - open-h-rf
- - beamforming
- - channel-data
-size_categories:
- - n<1K
----
-
-# UltraSound ToolBox (USTB) Channel Capture Data, PULSE Lab, Johns Hopkins University
-
-
-
-*First frames of all four acquisitions, reconstructed from the raw channel data with `reconstruct.py`. Top: [`alpinion_phantom/JHU_Alpinion_L3-8_CPWC_hypoechoic`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_CPWC_hypoechoic.hdf5), [`alpinion_phantom/JHU_Alpinion_L3-8_FI_hypoechoic`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_FI_hypoechoic.hdf5). Bottom: [`alpinion_phantom/JHU_Alpinion_L3-8_CPWC_hyperechoic_scatterers`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_CPWC_hyperechoic_scatterers.hdf5), [`alpinion_phantom/JHU_Alpinion_L3-8_FI_hyperechoic_scatterers`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_FI_hyperechoic_scatterers.hdf5). Panels are scaled to a common height per row, not to a common physical scale.*
-
-## Dataset Description
-
-Contributed by the **Photoacoustic & Ultrasonic Systems Engineering (PULSE) Lab, Johns Hopkins University (JHU)** to the [Open-H-RF](https://github.com/open-h/Open-H-RF) initiative. The data is owned by JHU. All data is pre-beamformed (raw channel-capture) ultrasound stored in the **zea** HDF5 format and released under **CC BY 4.0**.
-
-These acquisitions are part of the **UltraSound ToolBox (USTB) Channel Capture Collection**; the rest of that collection is in [`oslo/`](../oslo/README.md).
-
-4 acquisitions packaged into a single application sub-dataset. The sub-dataset folder contains its zea `.hdf5` files, a Hugging Face–style `README.md` data card, and reference B-mode images. The **`reconstruct.py`, `parameters.yaml` and `pipeline*.yaml` files live once at the root of this collection** (`jhu-pulselab/`); the sub-dataset folder also holds a `pipeline.yaml` for its example acquisition. The data card declares `license: cc-by-4.0` in its YAML frontmatter.
-
-| Folder | Sub-dataset | Tier | RFP task | Acq. |
-|---|---|---|---|---|
-| [`alpinion_phantom/`](alpinion_phantom/README.md) | Phantom (Alpinion L3-8) | phantom | 6.1 Generalized Reconstruction | 4 |
-
-Total: **4 acquisitions**, 0.47 GB of stored HDF5 data.
-
-## Dataset Contributor(s)
-
-- Muyinatu A. Lediju Bell (primary contact)
-- Ole Marius Hoel Rindal
-- Photoacoustic & Ultrasonic Systems Engineering (PULSE) Lab, Johns Hopkins University
-
-## Dataset Creation Date
-
-06/23/2026 (packaging date; the original acquisitions were made at the PULSE Lab, Johns Hopkins University, in 2017).
-
-## License / Terms of Use
-
-[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode.en). Retain attribution and identify modifications when reusing the data.
-
-## Processing the Dataset
-
-Every acquisition is reconstructable from the file alone — all acquisition parameters live in the zea `/scan` and `/probe` groups. The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/jhu-pulselab/reconstruct.py), as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline*.yaml` and `parameters.yaml` definitions in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub: set `PATHS` to the acquisition to reconstruct and `REFOCUS = True` to also emit the REFoCUS variant (the script and pipelines are the same as the USTB collection's; see its card's [Reconstruction Details](../oslo/README.md#reconstruction-details)). The sub-dataset folder also holds a `pipeline.yaml` for its example acquisition, so it can be rendered with a single `zea process` command (see the [sub-dataset card](alpinion_phantom/README.md)).
-
-## Dataset Format
-
-[zea v0.1.6](https://github.com/tue-bmd/zea)
-
-## Dataset Quantification
-
-**Current Open-H-RF release:** 4 HDF5 files; 466.42 MB (466,419,712 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
-
-## Citation
-
-Please credit the PULSE Lab, Johns Hopkins University, as the owner of this data, and cite the UltraSound ToolBox (USTB) proceedings paper:
-
-A. Rodriguez-Molares, O. M. H. Rindal, O. Bernard, A. Nair, M. A. Lediju Bell, H. Liebgott, A. Austeng and L. Løvstakken, "The UltraSound ToolBox," in *2017 IEEE International Ultrasonics Symposium (IUS)*, 2017, pp. 1–4. doi:[10.1109/ULTSYM.2017.8092389](https://doi.org/10.1109/ULTSYM.2017.8092389)
diff --git a/jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_CPWC_hyperechoic_scatterers.hdf5 b/jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_CPWC_hyperechoic_scatterers.hdf5
deleted file mode 100644
index f9b67eb6ebbd83e8d2e40a1007f5cdaa916fee71..0000000000000000000000000000000000000000
--- a/jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_CPWC_hyperechoic_scatterers.hdf5
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1fb6ea2449dc40ffc0f7ae355f525788025fa519648455df8cc64f756d110831
-size 40894464
diff --git a/jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_CPWC_hypoechoic.hdf5 b/jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_CPWC_hypoechoic.hdf5
deleted file mode 100644
index efabc97279214a142add0a02d14bf8edf022feb2..0000000000000000000000000000000000000000
--- a/jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_CPWC_hypoechoic.hdf5
+++ /dev/null
@@ -1,3 +0,0 @@
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--- a/jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_FI_hyperechoic_scatterers.hdf5
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deleted file mode 100644
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diff --git a/jhu-pulselab/alpinion_phantom/README.md b/jhu-pulselab/alpinion_phantom/README.md
deleted file mode 100644
index 595711222e5025d7286b16fe6a83912f9a6490d8..0000000000000000000000000000000000000000
--- a/jhu-pulselab/alpinion_phantom/README.md
+++ /dev/null
@@ -1,136 +0,0 @@
----
-name: jhu-pulselab-alpinion-phantom
-pretty_name: "USTB - Phantom (Alpinion L3-8, PULSE Lab, Johns Hopkins University)"
-license: cc-by-4.0
-task_categories:
- - image-to-image
-language:
- - en
-tags:
- - ultrasound
- - rf
- - open-h-rf
- - phantom
- - alpinion
- - ustb
-size_categories:
- - n<1K
----
-
-# USTB - Phantom (Alpinion L3-8, PULSE Lab, Johns Hopkins University)
-
-
-
-*First frames of all four acquisitions, reconstructed from the raw channel data with `reconstruct.py`. Top: [`JHU_Alpinion_L3-8_CPWC_hypoechoic`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_CPWC_hypoechoic.hdf5), [`JHU_Alpinion_L3-8_FI_hypoechoic`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_FI_hypoechoic.hdf5). Bottom: [`JHU_Alpinion_L3-8_CPWC_hyperechoic_scatterers`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_CPWC_hyperechoic_scatterers.hdf5), [`JHU_Alpinion_L3-8_FI_hyperechoic_scatterers`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_FI_hyperechoic_scatterers.hdf5). Panels are scaled to a common height per row, not to a common physical scale.*
-
-## Dataset Description
-
-Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** (see the [PULSE Lab collection card](../README.md) and the [USTB collection card](../../oslo/README.md)).
-
-**Data owner: Johns Hopkins University (JHU).** The data were acquired by Muyinatu A. Lediju Bell and Ole Marius Hoel Rindal at the Photoacoustic & Ultrasonic Systems Engineering (PULSE) Lab, JHU, on the PULSE Lab's Alpinion E-Cube 12R research scanner.
-
-Phantom channel-capture data of a CIRS General Purpose Ultrasound Phantom, acquired with an L3-8 linear-array probe. Hypoechoic and hyperechoic targets imaged with focused (FI) and coherent plane-wave compounding (CPWC) sequences.
-
-These recordings have been publicly available through the UltraSound ToolBox (USTB) since 2017, with M. A. Lediju Bell and O. M. H. Rindal as authors. The University of Oslo (UiO) converted the data to the zea format and prepared it for Open-H-RF. Open-H-RF `v1.0` published these four acquisitions as `oslo/D_alpinion_phantom/`; they now live under `jhu-pulselab/`, and each file credits JHU in its `/metadata/credit` and `description` fields.
-
-## Dataset Contributor(s)
-
-- Johns Hopkins University (JHU), data owner
-- Muyinatu A. Lediju Bell , PULSE Lab, JHU (primary contact)
-- Ole Marius Hoel Rindal , University of Oslo (UiO), Department of Informatics (data acquisition at the PULSE Lab; zea conversion)
-
-## Dataset Creation Date
-
-Acquired at the PULSE Lab, JHU, in 2017. Packaged in zea format on 06/23/2026.
-
-## License / Terms of Use
-
-[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode.en). Retain attribution and identify modifications when reusing the data. This data was first released with the USTB proceedings paper (Rodriguez-Molares et al., *The UltraSound ToolBox*, IEEE IUS 2017, doi:[10.1109/ULTSYM.2017.8092389](https://doi.org/10.1109/ULTSYM.2017.8092389)). Attribution under CC BY 4.0 means crediting the PULSE Lab, Johns Hopkins University, and citing the above paper (see [Citation](#citation)).
-
-## Intended Usage
-
-Generalized reconstruction and image-quality assessment on a second hardware platform (RFP task 6.1); cross-vendor robustness studies. (Open-H-RF RFP task 6.1 Generalized Reconstruction).
-
-## Dataset Characterization
-
-- **Data Collection Method:** phantom
-- **Labeling Method:** Derived (known phantom target types).
-- **Acquisition system:** probe(s) L3-8; element positions stored in `/probe/probe_geometry` (meters); center frequency, sampling frequency and sound speed stored per acquisition in `/scan` (see per-sample feature table).
-
-## Processing the Dataset
-
-The example acquisition can be processed with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea).
-
-`zea` streams the data from the Hugging Face Hub and processes it according to the pipeline. You can try it out with the following command:
-
-```bash
-zea process \
- --dataset hf://nvidia/Open-H-RF/jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_CPWC_hypoechoic.hdf5 \
- --config hf://nvidia/Open-H-RF/jhu-pulselab/alpinion_phantom/pipeline.yaml
-```
-
-This `pipeline.yaml` holds the pipeline and display window of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/jhu-pulselab/reconstruct.py) at the root of this collection (`jhu-pulselab/`), as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline*.yaml` and `parameters.yaml` definitions there and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub (see the [PULSE Lab collection card](../README.md#processing-the-dataset)).
-
-## Dataset Format
-
-[zea v0.1.6](https://github.com/tue-bmd/zea)
-
-All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file is a single acquisition with raw channel data `/data/raw_data` of shape `(n_frames, n_tx, n_ax, n_el, n_ch)` and a fully populated `/scan` group describing the transmit sequence (delays, focus distances, steering angles, apodization, timing). Data type: RF (n_ch=1). No demodulation or decimation was applied during packaging beyond conversion from the USTB Ultrasound File Format (UFF) to zea; RF data is demodulated inside the reconstruction pipeline.
-
-## Dataset Quantification
-
-**Current Open-H-RF release:** 4 HDF5 files; 466.42 MB (466,419,712 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
-
-- **Number of acquisitions:** 4
-- **Total channel-capture frames:** 4
-- **Train / validation / test split:** not predefined (research dataset).
-- **Stored HDF5 size:** 466.42 MB (466,419,712 bytes).
-
-Per-acquisition summary:
-
-| Acquisition | frames | transmits | samples | elements | n_ch | fs (MHz) | fc (MHz) | size (MB) |
-|---|---|---|---|---|---|---|---|---|
-| `JHU_Alpinion_L3-8_CPWC_hyperechoic_scatterers` | 1 | 21 | 4352 | 128 | 1 | 40.0 | 6.00 | 40.89 |
-| `JHU_Alpinion_L3-8_CPWC_hypoechoic` | 1 | 21 | 4352 | 128 | 1 | 40.0 | 6.00 | 40.96 |
-| `JHU_Alpinion_L3-8_FI_hyperechoic_scatterers` | 1 | 256 | 3474 | 128 | 1 | 40.0 | 6.00 | 192.35 |
-| `JHU_Alpinion_L3-8_FI_hypoechoic` | 1 | 256 | 3474 | 128 | 1 | 40.0 | 6.00 | 192.22 |
-
-Per-sample feature table:
-
-| Field | Shape | Dtype | Units | Description |
-|---|---|---|---|---|
-| `data/raw_data` | `(n_frames, n_tx, n_ax, n_el, n_ch)` | float32 | a.u. | Raw pre-beamformed RF channel data |
-| `scan/sampling_frequency` | `scalar` | float32 | Hz | A/D sampling frequency |
-| `scan/center_frequency` | `scalar` | float32 | Hz | Transmit pulse center frequency |
-| `scan/demodulation_frequency` | `scalar` | float32 | Hz | Demodulation (carrier) frequency |
-| `scan/sound_speed` | `scalar` | float32 | m/s | Assumed medium speed of sound |
-| `scan/initial_times` | `(n_tx,)` | float32 | s | A/D start time per transmit |
-| `scan/t0_delays` | `(n_tx, n_el)` | float32 | s | Per-element transmit fire times |
-| `scan/tx_apodizations` | `(n_tx, n_el)` | float32 | - | Per-element transmit apodization |
-| `scan/focus_distances` | `(n_tx,)` | float32 | m | Focus distance per transmit (0 = plane wave) |
-| `scan/polar_angles` | `(n_tx,)` | float32 | rad | Transmit steering (polar) angle |
-| `scan/transmit_origins` | `(n_tx, 3)` | float32 | m | Transmit beam origin (x, y, z) |
-| `probe/probe_geometry` | `(n_el, 3)` | float32 | m | Element positions (x, y, z) |
-| `metadata/credit` | `scalar` | string | - | Attribution: "Acquired by M. A. Lediju Bell and O. M. H. Rindal at the PULSE Lab, Johns Hopkins University (JHU). Owned by JHU." |
-
-## Subject Metadata
-
-No human or animal subjects. Targets: hypoechoic/hyperechoic inclusions and point scatterers in a CIRS General Purpose Ultrasound Phantom. Scanner: Alpinion E-Cube 12R (PULSE Lab, JHU). Probe: L3-8 linear array (128 elements).
-
-## Data Validation
-
-A Delay-And-Sum `zea.Pipeline` (`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`) reconstructs every acquisition as a portable check that the recorded geometry and timing are correct (see [Processing the Dataset](#processing-the-dataset)).
-
-## Known Issues
-
-Mixed transmit schemes across files (CPWC / FI).
-
-## Ethical Considerations
-
-Phantom acquisitions; no human or animal subjects. No ethical considerations beyond standard laboratory practice.
-
-## Citation
-
-Please credit the PULSE Lab, Johns Hopkins University, as the owner of this data, and cite:
-
-A. Rodriguez-Molares, O. M. H. Rindal, O. Bernard, A. Nair, M. A. Lediju Bell, H. Liebgott, A. Austeng and L. Løvstakken, "The UltraSound ToolBox," in *2017 IEEE International Ultrasonics Symposium (IUS)*, 2017, pp. 1–4. doi:[10.1109/ULTSYM.2017.8092389](https://doi.org/10.1109/ULTSYM.2017.8092389)
diff --git a/jhu-pulselab/alpinion_phantom/assets/hero.png b/jhu-pulselab/alpinion_phantom/assets/hero.png
deleted file mode 100644
index adf82a162f64f496f7363985006278635a2de5bc..0000000000000000000000000000000000000000
--- a/jhu-pulselab/alpinion_phantom/assets/hero.png
+++ /dev/null
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diff --git a/jhu-pulselab/assets/hero.png b/jhu-pulselab/assets/hero.png
deleted file mode 100644
index adf82a162f64f496f7363985006278635a2de5bc..0000000000000000000000000000000000000000
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diff --git a/jhu-pulselab/parameters.yaml b/jhu-pulselab/parameters.yaml
deleted file mode 100644
index ce24099737322b7de97840845d42c727e94fa9bb..0000000000000000000000000000000000000000
--- a/jhu-pulselab/parameters.yaml
+++ /dev/null
@@ -1,32 +0,0 @@
-# Per-acquisition reconstruction parameters, used by reconstruct.py. For each acquisition:
-# pipeline -- which reconstruction to use:
-# scanline (focused linear FI, per-beam, pipeline_scanline.yaml),
-# compound (plane-wave CPWC, pipeline.yaml).
-# zlims/xlims -- display window in mm (framing only).
-# dynamic_range -- display dynamic range in dB (default [-60, 0]).
-# refocus -- if true, also emit a REFoCUS variant (_zea_refocus_bmode.png,
-# pipeline_refocus.yaml). REFoCUS (Bottenus 2018) inverts the
-# transmit-encoding matrix to recover the multistatic dataset before DAS.
-
-# alpinion_phantom
-JHU_Alpinion_L3-8_CPWC_hyperechoic_scatterers:
- pipeline: compound
- refocus: true
- zlims: [3, 55]
- xlims: [-19, 19]
-JHU_Alpinion_L3-8_CPWC_hypoechoic:
- pipeline: compound
- refocus: true
- zlims: [5, 50]
- xlims: [-19, 19]
-JHU_Alpinion_L3-8_FI_hyperechoic_scatterers:
- pipeline: scanline
- refocus: true
- zlims: [3, 55]
- xlims: [-19, 19]
-JHU_Alpinion_L3-8_FI_hypoechoic:
- pipeline: scanline
- refocus: true
- zlims: [0, 60]
- xlims: [-19, 19]
- dynamic_range: [-50, 0]
diff --git a/jhu-pulselab/pipeline.yaml b/jhu-pulselab/pipeline.yaml
deleted file mode 100644
index cf834c7ac61ae052761c997fcd0dca7cf2c08920..0000000000000000000000000000000000000000
--- a/jhu-pulselab/pipeline.yaml
+++ /dev/null
@@ -1,28 +0,0 @@
-# B-mode pipeline for the plane-wave compounding (CPWC) acquisitions. Plane
-# waves insonify the whole field of view, so pressure-field weighting is
-# disabled (equal coherent compounding).
-#
-# apply_window tapers/zeros the start of each RF line to suppress the transmit
-# near-field ringdown and receive sidelobes.
-
-parameters:
- f_number: 1.75
- grid_size_x: 400
- grid_size_z: 600
- dynamic_range: [-60, 0]
- selected_transmits: all
-
-pipeline:
- operations:
- - name: keras.ops.cast
- params:
- dtype: float32
- - name: apply_window
- - name: demodulate
- - name: beamform
- params:
- beamformer: delay_and_sum
- enable_pfield: false
- - name: envelope_detect
- - name: normalize
- - name: log_compress
diff --git a/jhu-pulselab/pipeline_refocus.yaml b/jhu-pulselab/pipeline_refocus.yaml
deleted file mode 100644
index de86bbb45c830b0c684a60c0c97e392e3c563e43..0000000000000000000000000000000000000000
--- a/jhu-pulselab/pipeline_refocus.yaml
+++ /dev/null
@@ -1,42 +0,0 @@
-# B-mode pipeline with REFoCUS transmit-encoding recovery, for non-focused
-# linear-array acquisitions that have many transmit events (CPWC and focused FI).
-#
-# REFoCUS (Bottenus 2018) inverts the transmit-encoding matrix in the frequency
-# domain to recover the multistatic (per-element / full-matrix-capture) dataset
-# from the encoded transmit sequence; that synthetic aperture is then beamformed
-# with pressure-field-weighted Delay-And-Sum. Compared with directly compounding
-# the encoded transmits (pipeline.yaml) this can sharpen lateral resolution and
-# contrast. The inversion needs enough transmit events to be well posed (>= 8),
-# so this pipeline is only applied to the many-angle acquisitions flagged with
-# `refocus: true` in parameters.yaml (the standard compound reconstruction is
-# still produced for those files as well).
-#
-# jit_compile is disabled on refocus to avoid a cuBLASLt autotuning failure on
-# the complex matmul under XLA on some GPUs.
-
-parameters:
- f_number: 1.75
- grid_size_x: 400
- grid_size_z: 600
- dynamic_range: [-60, 0]
- selected_transmits: all
-
-pipeline:
- operations:
- - name: keras.ops.cast
- params:
- dtype: float32
- - name: apply_window
- - name: refocus
- params:
- method: adjoint
- param: null
- jit_compile: false
- - name: demodulate
- - name: beamform
- params:
- beamformer: delay_and_sum
- enable_pfield: true
- - name: envelope_detect
- - name: normalize
- - name: log_compress
diff --git a/jhu-pulselab/pipeline_scanline.yaml b/jhu-pulselab/pipeline_scanline.yaml
deleted file mode 100644
index 1b0ad7247612225c34d15b3eb4db4e166b78c54a..0000000000000000000000000000000000000000
--- a/jhu-pulselab/pipeline_scanline.yaml
+++ /dev/null
@@ -1,26 +0,0 @@
-# Scanline (line-by-line) B-mode for focused linear (FI) scans: each focused
-# transmit forms one image line (no cross-transmit compounding).
-
-parameters:
- f_number: 1.75
- dynamic_range: [-60, 0]
- enable_scanline: true
- grid_type: cartesian
- selected_transmits: all
- grid_size_z: 700
-
-pipeline:
- operations:
- - name: keras.ops.cast
- params:
- dtype: float32
- - name: apply_window
- - name: demodulate
- - name: beamform
- params:
- beamformer: delay_and_sum
- enable_aligned_apodization: true
- enable_pfield: false
- - name: envelope_detect
- - name: normalize
- - name: log_compress
diff --git a/kaist-snubh-barreleye/README.md b/kaist-snubh-barreleye/README.md
index 63a93b4c81954f334019d1b897a57426c0729a38..a41ece6c529a01233fff4c47157f3e0cb672c62c 100644
--- a/kaist-snubh-barreleye/README.md
+++ b/kaist-snubh-barreleye/README.md
@@ -8,7 +8,7 @@ task_categories:
tags:
- ultrasound
- rf
- - open-h-rf
+ - openh-rf
- breast
- in-vivo
- plane-wave
@@ -23,7 +23,7 @@ size_categories:
-*Delay-and-sum reconstruction of a biopsy-proven invasive ductal carcinoma, [`data/S01_D1.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/kaist-snubh-barreleye/data/S01_D1.hdf5).*
+*Delay-and-sum reconstruction of a biopsy-proven invasive ductal carcinoma, [`data/S01_D1.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/kaist-snubh-barreleye/data/S01_D1.hdf5).*
## Dataset Description
@@ -70,13 +70,13 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
```bash
zea process \
- --dataset hf://nvidia/Open-H-RF/kaist-snubh-barreleye/data/S01_D1.hdf5 \
- --config hf://nvidia/Open-H-RF/kaist-snubh-barreleye/pipeline.yaml \
+ --dataset hf://nvidia/OpenH-RF/kaist-snubh-barreleye/data/S01_D1.hdf5 \
+ --config hf://nvidia/OpenH-RF/kaist-snubh-barreleye/pipeline.yaml \
--n-frames 1 \
--save-as png
```
-Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/kaist-snubh-barreleye/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF).
+Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/kaist-snubh-barreleye/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF).
This is a single-frame acquisition, so `zea process` outputs a `.png` rather than a `.gif` — this requires a `zea` build newer than the currently pinned 0.1.6 (single-frame PNG output landed after that release).
@@ -84,7 +84,7 @@ This is a single-frame acquisition, so `zea process` outputs a `.png` rather tha
[zea v0.1.6](https://github.com/tue-bmd/zea)
-All data is delivered in the **zea HDF5** format (Open-H-RF spec). One HDF5 file per acquisition; one image track per file.
+All data is delivered in the **zea HDF5** format (OpenH-RF spec). One HDF5 file per acquisition; one image track per file.
```
hdf5/
@@ -94,14 +94,14 @@ hdf5/
```
**`original/` — raw RF channel data.**
-- **Channel reordering** of the scanner's raw export into the Open-H-RF convention `(n_frames=1, n_tx=9, n_ax=Ns, n_el=192, n_ch=1)`.
+- **Channel reordering** of the scanner's raw export into the OpenH-RF convention `(n_frames=1, n_tx=9, n_ax=Ns, n_el=192, n_ch=1)`.
- No demodulation, decimation, band-pass filtering, or value clipping — `raw_data` is bit-faithful to the scanner export.
## Dataset Quantification
-**Current Open-H-RF release:** 70 HDF5 files; 802.10 MB (802,095,104 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 70 HDF5 files; 802.10 MB (802,095,104 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
-- **Subjects:** 35 (S01–S37 with S11 and S29 unused, all female; age groups in *Subject Metadata*).
+- **Subjects:** 35 (S01–S35, all female; age groups in *Subject Metadata*).
- **Acquisitions:** 70 (2 repeat acquisitions per subject, named `_D1` / `_D2`).
- **HDF5 files:** 70 (one file per acquisition in `hdf5/original/`).
- **RF frames:** 70 (one acquired frame per acquisition).
@@ -144,13 +144,14 @@ Per-file metadata follows the **HIPAA Safe-Harbor** approach: only **de-identifi
| Age group | Count | % |
|---|---|---|
| 20-29 | 2 | 5.7% |
-| 30-39 | 5 | 14.3% |
+| 30-39 | 4 | 11.4% |
| 40-49 | 12 | 34.3% |
| 50-59 | 8 | 22.9% |
| 60-69 | 5 | 14.3% |
| 70-79 | 3 | 8.6% |
+| 80-89 | 1 | 2.9% |
-- **Case status:** 23 benign (65.7%) / 12 malignant (34.3%)
+- **Case status:** 22 benign (62.9%) / 13 malignant (37.1%)
- **BI-RADS distribution:**
| BI-RADS | Count | % |
@@ -158,23 +159,22 @@ Per-file metadata follows the **HIPAA Safe-Harbor** approach: only **de-identifi
| C2 | 8 | 22.9% |
| C3 | 5 | 14.3% |
| C4a | 11 | 31.4% |
-| C4b | 1 | 2.9% |
-| C4c | 1 | 2.9% |
+| C4b | 2 | 5.7% |
| C5 | 2 | 5.7% |
| C6 | 7 | 20.0% |
-- **Lesion size (longest axis, cm):** n=35, min 0.50, max 5.20, mean 1.36, median 1.00.
+- **Lesion size (longest axis, cm):** n=35, min 0.50, max 5.20, mean 1.40, median 1.00.
| Lesion size | Count |
|---|---|
-| < 1.0 cm | 17 |
-| 1.0 – 2.0 cm | 10 |
-| 2.0 – 3.0 cm | 6 |
-| ≥ 3.0 cm | 2 |
+| < 1.0 cm | 14 |
+| 1.0 – 2.0 cm | 13 |
+| 2.0 – 3.0 cm | 5 |
+| ≥ 3.0 cm | 3 |
- **Histopathology subtypes:**
- - *Malignant (n=12):* IDC (8), DCIS (4)
- - *Benign (n=23):* FA (8), IDP (3), FCC (1), Usual Ductal Hyperplasia (1), ADH (1), unspecified / NA (9)
+ - *Malignant (n=13):* IDC (8), DCIS (4), ADH (1)
+ - *Benign (n=22):* FA (9), IDP (3), FCC (1), Usual Ductal Hyperplasia (1), unspecified / NA (8)
- **Anatomical region:** Breast (left and right; mixed lesion locations).
- **Scanner / probe model:** Clinical FDA-cleared scanner with a 192-element linear 10 MHz probe.
@@ -205,6 +205,6 @@ Reference output: `main.png` — `data/S01_D1.hdf5` (biopsy-proven invasive duct
## Ethical Considerations
- **Consent status:** All subjects gave informed consent under SNUBH IRB protocol **B-2401-876-301**.
-- **De-identification:** No direct identifiers (name, full exam date, free-text clinical notes) are stored. Age is decade-binned at the dataset level (not stored per file); exact lesion size and exam dates are not stored per file; only the acquisition year (2024) is reported. Subject IDs are coded (`S01`…`S37`; `S11` and `S29` are unused).
+- **De-identification:** No direct identifiers (name, full exam date, free-text clinical notes) are stored. Age is decade-binned at the dataset level (not stored per file); exact lesion size and exam dates are not stored per file; only the acquisition year (2024) is reported. Subject IDs are coded (`S01`…`S35`).
- **IRB approval:** SNUBH IRB **B-2401-876-301**
- **Animal welfare (ARRIVE 2.0):** Not applicable — human-only dataset.
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diff --git a/mosaic-nuevosono/README.md b/mosaic-intelligence/README.md
similarity index 91%
rename from mosaic-nuevosono/README.md
rename to mosaic-intelligence/README.md
index 7b006824675a729d5f0e50540adb6cef6d2dc2a5..0b0405c85e4f00cfffa3488235b51c8ea73b0dd7 100644
--- a/mosaic-nuevosono/README.md
+++ b/mosaic-intelligence/README.md
@@ -1,5 +1,5 @@
---
-name: mosaic-nuevosono
+name: mosaic-intelligence
pretty_name: "Mosaic Intelligence / NuevoSono IVUS"
license: cc-by-4.0
task_categories:
@@ -7,7 +7,7 @@ task_categories:
tags:
- ultrasound
- rf
- - open-h-rf
+ - openh-rf
- IVUS
- tracked-ultrasound
language:
@@ -18,7 +18,7 @@ language:
-*Pullback through [`data/22_12_10_52.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/mosaic-nuevosono/data/22_12_10_52.hdf5), reconstructed from the raw channel data. Left to right: B-mode, the same frame with the lumen and intima-media segmentation, and the linear-encoder pullback position with the current frame marked.*
+*Pullback through [`data/22_12_10_52.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/mosaic-intelligence/data/22_12_10_52.hdf5), reconstructed from the raw channel data. Left to right: B-mode, the same frame with the lumen and intima-media segmentation, and the linear-encoder pullback position with the current frame marked.*
## Dataset Description
@@ -52,7 +52,7 @@ Intended for IVUS tracking and segmentation applications, including lesion detec
## Processing the Dataset
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/mosaic-intelligence/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/mosaic-intelligence/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
The script overlays the segmentation masks on frames spread evenly across the pullback:
@@ -139,6 +139,6 @@ A `zea.Pipeline` reconstructs the IVUS B-mode from the raw channel data (RF →
## Ethical Considerations
-Porcine animal study data only; contains no human subjects or PHI. Collected and released in compliance with applicable institutional animal care approvals and Open-H-RF governance and data-sharing policies.
+Porcine animal study data only; contains no human subjects or PHI. Collected and released in compliance with applicable institutional animal care approvals and OpenH-RF governance and data-sharing policies.
All contributed data, labels and metadata are released by Mosaic Intelligence Labs and NuevoSono; pre-existing hardware, software, simulation and platform intellectual property remains the property of the respective owners. The license is also recorded in each file's `metadata/credit` field.
diff --git a/mosaic-nuevosono/assets/15_10_18_21/overview_5_frames.png b/mosaic-intelligence/assets/15_10_18_21/overview_5_frames.png
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rename to mosaic-intelligence/assets/main.png
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rename from mosaic-nuevosono/assets/pullback.gif
rename to mosaic-intelligence/assets/pullback.gif
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rename to mosaic-intelligence/data/15_10_18_21.hdf5
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rename to mosaic-intelligence/data/15_10_50_19.hdf5
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rename to mosaic-intelligence/data/22_12_10_52.hdf5
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rename to mosaic-intelligence/data/22_12_29_46.hdf5
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rename to mosaic-intelligence/data/22_12_38_45.hdf5
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rename from mosaic-nuevosono/data/22_13_10_16.hdf5
rename to mosaic-intelligence/data/22_13_10_16.hdf5
diff --git a/mosaic-nuevosono/data/22_13_56_43.hdf5 b/mosaic-intelligence/data/22_13_56_43.hdf5
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rename from mosaic-nuevosono/data/22_13_56_43.hdf5
rename to mosaic-intelligence/data/22_13_56_43.hdf5
diff --git a/mosaic-nuevosono/data/22_14_29_54.hdf5 b/mosaic-intelligence/data/22_14_29_54.hdf5
similarity index 100%
rename from mosaic-nuevosono/data/22_14_29_54.hdf5
rename to mosaic-intelligence/data/22_14_29_54.hdf5
diff --git a/mosaic-nuevosono/pipeline.yaml b/mosaic-intelligence/pipeline.yaml
similarity index 100%
rename from mosaic-nuevosono/pipeline.yaml
rename to mosaic-intelligence/pipeline.yaml
diff --git a/nv-raw2insights-us/README.md b/nv-raw2insights-us/README.md
index f3828fc9b5b23ce561da2c73dbfb7726db6db7f3..549d6be789ab93276404fd990dd3dd4b52b92db5 100644
--- a/nv-raw2insights-us/README.md
+++ b/nv-raw2insights-us/README.md
@@ -8,7 +8,7 @@ tags:
- ultrasound
- rf
- iq
- - open-h-rf
+ - openh-rf
- beamforming
- sound-speed-estimation
- phase-aberration
@@ -24,7 +24,7 @@ size_categories:
-*DBUA results using synthetic validation sample [`data/nv_r2i_us_validation_0084.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/nv-raw2insights-us/data/nv_r2i_us_validation_0084.hdf5): B-mode (left) and estimated sound speed (right). Bulk-speed calibration is followed by 400 spatial-refinement iterations, with fixed display scales. These are DBUA reconstructions, not predictions from an NV-Raw2Insights-US model.*
+*DBUA results using synthetic validation sample [`data/nv_r2i_us_validation_0084.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/nv-raw2insights-us/data/nv_r2i_us_validation_0084.hdf5): B-mode (left) and estimated sound speed (right). Bulk-speed calibration is followed by 400 spatial-refinement iterations, with fixed display scales. These are DBUA reconstructions, not predictions from an NV-Raw2Insights-US model.*
@@ -34,7 +34,7 @@ NV-Raw2Insights-US is a **simulated full-synthetic-aperture (FSA)** ultrasound d
Each sample is a single-frame FSA acquisition from a **180-element linear array** simulated with **k-Wave** over a heterogeneous tissue phantom containing cysts. Every acquisition provides raw baseband **IQ channel data** alongside co-registered ground truth: a **speed-of-sound map**, a **binary cyst segmentation mask**, a **DAS B-mode** and a **focused-transmit B-mode**, and a per-sample **phase-aberration** value. All data are **synthetic**; no human or animal subjects are involved.
-This is the **zea-format (Open-H-RF) release**; the same simulations are also published in a Hugging Face `datasets`/Arrow build at [`nvidia/NV-Raw2Insights-US`](https://huggingface.co/datasets/nvidia/NV-Raw2Insights-US).
+This is the **zea-format (OpenH-RF) release**; the same simulations are also published in a Hugging Face `datasets`/Arrow build at [`nvidia/NV-Raw2Insights-US`](https://huggingface.co/datasets/nvidia/NV-Raw2Insights-US).
## Dataset Contributor(s)
@@ -60,7 +60,7 @@ Developing and benchmarking methods that operate on raw ultrasound channel data:
## Processing the Dataset
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/nv-raw2insights-us/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), which uses the [zea library](https://github.com/tue-bmd/zea) and streams the data from the Hugging Face Hub. It reconstructs both a plain DAS B-mode (the `pipeline.yaml` in this folder) and a sound-speed-corrected B-mode using the ground-truth `sos_map`.
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/nv-raw2insights-us/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), which uses the [zea library](https://github.com/tue-bmd/zea) and streams the data from the Hugging Face Hub. It reconstructs both a plain DAS B-mode (the `pipeline.yaml` in this folder) and a sound-speed-corrected B-mode using the ground-truth `sos_map`.
## Dataset Format
@@ -87,7 +87,7 @@ Per-file contents:
## Dataset Quantification
-**Current Open-H-RF release:** 923 HDF5 files; 214.13 GB (214,133,178,368 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 923 HDF5 files; 214.13 GB (214,133,178,368 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
- **923 samples (830 train / 93 validation)**, one HDF5 per sample, all in `data/`; the split is carried in the filename.
- Each sample is a single-frame FSA acquisition (frame index always 0).
diff --git a/oslo/A_cardiac/README.md b/oslo/A_cardiac/README.md
index 32e32dbb775f4a793291d6634023b154d160d533..86348d2d8dc476190d7f9e5e0ec55cca7181a42e 100644
--- a/oslo/A_cardiac/README.md
+++ b/oslo/A_cardiac/README.md
@@ -9,7 +9,7 @@ language:
tags:
- ultrasound
- rf
- - open-h-rf
+ - openh-rf
- cardiac
- in-vivo
size_categories:
@@ -20,7 +20,7 @@ size_categories:
-*All 50 frames of [`Verasonics_P2-4_parasternal_long_subject_1.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/A_cardiac/Verasonics_P2-4_parasternal_long_subject_1.hdf5), reconstructed from the raw channel data with `reconstruct.py`.*
+*All 50 frames of [`Verasonics_P2-4_parasternal_long_subject_1.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/A_cardiac/Verasonics_P2-4_parasternal_long_subject_1.hdf5), reconstructed from the raw channel data with `reconstruct.py`.*
## Dataset Description
@@ -46,7 +46,7 @@ In-vivo human cardiac channel-capture data acquired with a Verasonics Vantage 25
## Intended Usage
-Generalized reconstruction and adaptive beamforming of cardiac ultrasound (RFP task 6.1). Suitable for B-mode reconstruction, aperture-domain processing, and deep-learning beamforming research on in-vivo cardiac data. (Open-H-RF RFP task 6.1 Generalized Reconstruction).
+Generalized reconstruction and adaptive beamforming of cardiac ultrasound (RFP task 6.1). Suitable for B-mode reconstruction, aperture-domain processing, and deep-learning beamforming research on in-vivo cardiac data. (OpenH-RF RFP task 6.1 Generalized Reconstruction).
## Dataset Characterization
@@ -62,11 +62,11 @@ The example acquisition can be processed with the `pipeline.yaml` definition in
```bash
zea process \
- --dataset hf://nvidia/Open-H-RF/oslo/A_cardiac/Verasonics_P2-4_parasternal_long_subject_1.hdf5 \
- --config hf://nvidia/Open-H-RF/oslo/A_cardiac/pipeline.yaml
+ --dataset hf://nvidia/OpenH-RF/oslo/A_cardiac/Verasonics_P2-4_parasternal_long_subject_1.hdf5 \
+ --config hf://nvidia/OpenH-RF/oslo/A_cardiac/pipeline.yaml
```
-This `pipeline.yaml` holds the pipeline, display window and sector limits of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
+This `pipeline.yaml` holds the pipeline, display window and sector limits of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
## Dataset Format
@@ -76,7 +76,7 @@ All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file i
## Dataset Quantification
-**Current Open-H-RF release:** 2 HDF5 files; 3.53 GB (3,533,897,728 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 2 HDF5 files; 3.53 GB (3,533,897,728 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
- **Number of acquisitions:** 2
- **Total channel-capture frames:** 75
@@ -127,6 +127,4 @@ In-vivo data recorded from healthy adult volunteers at the University of Oslo wi
## Citation
-Please cite the UltraSound ToolBox (USTB) proceedings paper:
-
-A. Rodriguez-Molares, O. M. H. Rindal, O. Bernard, A. Nair, M. A. Lediju Bell, H. Liebgott, A. Austeng and L. Løvstakken, "The UltraSound ToolBox," in *2017 IEEE International Ultrasonics Symposium (IUS)*, 2017, pp. 1–4. doi:[10.1109/ULTSYM.2017.8092389](https://doi.org/10.1109/ULTSYM.2017.8092389)
+Please cite the UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo (Zenodo record 20261898).
diff --git a/oslo/B_carotid/README.md b/oslo/B_carotid/README.md
index 9a1603ded2675c38180a5da298e71c18f189711d..da265fd97570fdca6bc06fcc15a77faea61fb2b1 100644
--- a/oslo/B_carotid/README.md
+++ b/oslo/B_carotid/README.md
@@ -9,7 +9,7 @@ language:
tags:
- ultrasound
- rf
- - open-h-rf
+ - openh-rf
- vascular
- carotid
- in-vivo
@@ -21,7 +21,7 @@ size_categories:
-*First frame of [`L7_FI_carotid_cross_1.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/B_carotid/L7_FI_carotid_cross_1.hdf5), reconstructed from the raw channel data with `reconstruct.py`.*
+*First frame of [`L7_FI_carotid_cross_1.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/B_carotid/L7_FI_carotid_cross_1.hdf5), reconstructed from the raw channel data with `reconstruct.py`.*
## Dataset Description
@@ -47,7 +47,7 @@ In-vivo human carotid-artery channel-capture data acquired with a Verasonics Van
## Intended Usage
-Generalized reconstruction and adaptive beamforming of vascular ultrasound (RFP task 6.1). (Open-H-RF RFP task 6.1 Generalized Reconstruction).
+Generalized reconstruction and adaptive beamforming of vascular ultrasound (RFP task 6.1). (OpenH-RF RFP task 6.1 Generalized Reconstruction).
## Dataset Characterization
@@ -63,11 +63,11 @@ The example acquisition can be processed with the `pipeline.yaml` definition in
```bash
zea process \
- --dataset hf://nvidia/Open-H-RF/oslo/B_carotid/L7_FI_carotid_cross_1.hdf5 \
- --config hf://nvidia/Open-H-RF/oslo/B_carotid/pipeline.yaml
+ --dataset hf://nvidia/OpenH-RF/oslo/B_carotid/L7_FI_carotid_cross_1.hdf5 \
+ --config hf://nvidia/OpenH-RF/oslo/B_carotid/pipeline.yaml
```
-This `pipeline.yaml` holds the pipeline and display window of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
+This `pipeline.yaml` holds the pipeline and display window of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
## Dataset Format
@@ -77,7 +77,7 @@ All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file i
## Dataset Quantification
-**Current Open-H-RF release:** 3 HDF5 files; 219.48 MB (219,480,064 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 3 HDF5 files; 219.48 MB (219,480,064 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
- **Number of acquisitions:** 3
- **Total channel-capture frames:** 6
@@ -129,6 +129,4 @@ In-vivo data recorded from healthy adult volunteers at the University of Oslo wi
## Citation
-Please cite the UltraSound ToolBox (USTB) proceedings paper:
-
-A. Rodriguez-Molares, O. M. H. Rindal, O. Bernard, A. Nair, M. A. Lediju Bell, H. Liebgott, A. Austeng and L. Løvstakken, "The UltraSound ToolBox," in *2017 IEEE International Ultrasonics Symposium (IUS)*, 2017, pp. 1–4. doi:[10.1109/ULTSYM.2017.8092389](https://doi.org/10.1109/ULTSYM.2017.8092389)
+Please cite the UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo (Zenodo record 20261898).
diff --git a/oslo/C_verasonics_phantom/README.md b/oslo/C_verasonics_phantom/README.md
index 6455a319da9ac6ba2e83315d633ea773714b067e..0a73e2cddb03ff1c4c07d7fb71f75be80b88393f 100644
--- a/oslo/C_verasonics_phantom/README.md
+++ b/oslo/C_verasonics_phantom/README.md
@@ -9,7 +9,7 @@ language:
tags:
- ultrasound
- rf
- - open-h-rf
+ - openh-rf
- phantom
- cirs
size_categories:
@@ -20,7 +20,7 @@ size_categories:
-*First frame of [`FI_P4_cysts_center.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/C_verasonics_phantom/FI_P4_cysts_center.hdf5), reconstructed from the raw channel data with `reconstruct.py`.*
+*First frame of [`FI_P4_cysts_center.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/C_verasonics_phantom/FI_P4_cysts_center.hdf5), reconstructed from the raw channel data with `reconstruct.py`.*
## Dataset Description
@@ -46,7 +46,7 @@ Tissue-mimicking and table-top phantom channel-capture data acquired on a Veraso
## Intended Usage
-Generalized reconstruction, image-quality assessment (resolution, contrast, dynamic range), and beamforming research (RFP task 6.1). Several acquisitions provide multiple transmit schemes on the same target for cross-method comparison. (Open-H-RF RFP task 6.1 Generalized Reconstruction).
+Generalized reconstruction, image-quality assessment (resolution, contrast, dynamic range), and beamforming research (RFP task 6.1). Several acquisitions provide multiple transmit schemes on the same target for cross-method comparison. (OpenH-RF RFP task 6.1 Generalized Reconstruction).
## Dataset Characterization
@@ -62,11 +62,11 @@ The example acquisition can be processed with the `pipeline.yaml` definition in
```bash
zea process \
- --dataset hf://nvidia/Open-H-RF/oslo/C_verasonics_phantom/FI_P4_cysts_center.hdf5 \
- --config hf://nvidia/Open-H-RF/oslo/C_verasonics_phantom/pipeline.yaml
+ --dataset hf://nvidia/OpenH-RF/oslo/C_verasonics_phantom/FI_P4_cysts_center.hdf5 \
+ --config hf://nvidia/OpenH-RF/oslo/C_verasonics_phantom/pipeline.yaml
```
-This `pipeline.yaml` holds the pipeline, display window and sector limits of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
+This `pipeline.yaml` holds the pipeline, display window and sector limits of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
## Dataset Format
@@ -76,7 +76,7 @@ All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file i
## Dataset Quantification
-**Current Open-H-RF release:** 15 HDF5 files; 1.06 GB (1,058,734,080 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 15 HDF5 files; 1.06 GB (1,058,734,080 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
- **Number of acquisitions:** 15
- **Total channel-capture frames:** 27
@@ -140,6 +140,4 @@ Phantom / table-top acquisitions; no human or animal subjects are involved. No e
## Citation
-Please cite the UltraSound ToolBox (USTB) proceedings paper:
-
-A. Rodriguez-Molares, O. M. H. Rindal, O. Bernard, A. Nair, M. A. Lediju Bell, H. Liebgott, A. Austeng and L. Løvstakken, "The UltraSound ToolBox," in *2017 IEEE International Ultrasonics Symposium (IUS)*, 2017, pp. 1–4. doi:[10.1109/ULTSYM.2017.8092389](https://doi.org/10.1109/ULTSYM.2017.8092389)
+Please cite the UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo (Zenodo record 20261898).
diff --git a/oslo/D_alpinion_phantom/Alpinion_L3-8_CPWC_hyperechoic_scatterers.hdf5 b/oslo/D_alpinion_phantom/Alpinion_L3-8_CPWC_hyperechoic_scatterers.hdf5
new file mode 100644
index 0000000000000000000000000000000000000000..663daafca39fdabb41fd5fd59bb66edccefe568f
--- /dev/null
+++ b/oslo/D_alpinion_phantom/Alpinion_L3-8_CPWC_hyperechoic_scatterers.hdf5
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:21629385413a6a2ca5d679c3bddec8ea20a69006484a6ace9a2c049fb2e427ca
+size 40894464
diff --git a/oslo/D_alpinion_phantom/Alpinion_L3-8_CPWC_hypoechoic.hdf5 b/oslo/D_alpinion_phantom/Alpinion_L3-8_CPWC_hypoechoic.hdf5
new file mode 100644
index 0000000000000000000000000000000000000000..150cfe00f01883703292793b809ed7377bd60f7e
--- /dev/null
+++ b/oslo/D_alpinion_phantom/Alpinion_L3-8_CPWC_hypoechoic.hdf5
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aeaf2125f1f3febac500eaeca61cb743518fe6f580be1893e90f17ee6f46fd9b
+size 40960000
diff --git a/oslo/D_alpinion_phantom/Alpinion_L3-8_FI_hyperechoic_scatterers.hdf5 b/oslo/D_alpinion_phantom/Alpinion_L3-8_FI_hyperechoic_scatterers.hdf5
new file mode 100644
index 0000000000000000000000000000000000000000..3f30cb83cf7e005ec3ef875b25b251e5a0310c61
--- /dev/null
+++ b/oslo/D_alpinion_phantom/Alpinion_L3-8_FI_hyperechoic_scatterers.hdf5
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:67988844f41f728d2d0df7ef55168357f0e853817dc7d5282ea05329a53e21b8
+size 192348160
diff --git a/oslo/D_alpinion_phantom/Alpinion_L3-8_FI_hypoechoic.hdf5 b/oslo/D_alpinion_phantom/Alpinion_L3-8_FI_hypoechoic.hdf5
new file mode 100644
index 0000000000000000000000000000000000000000..166369fa2e31b175c58c608f0e68bd161ffe8769
--- /dev/null
+++ b/oslo/D_alpinion_phantom/Alpinion_L3-8_FI_hypoechoic.hdf5
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:524924d9820fc5b365c38ef0ff24ba77a3be1114b734cb6b9149f9ec731c0113
+size 192217088
diff --git a/oslo/D_alpinion_phantom/README.md b/oslo/D_alpinion_phantom/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..9f47e6e0930431a0379a5c17d01517a2710ee404
--- /dev/null
+++ b/oslo/D_alpinion_phantom/README.md
@@ -0,0 +1,132 @@
+---
+name: oslo-d-alpinion-phantom
+pretty_name: "USTB - Phantom (Alpinion L3-8)"
+license: cc-by-4.0
+task_categories:
+ - image-to-image
+language:
+ - en
+tags:
+ - ultrasound
+ - rf
+ - openh-rf
+ - phantom
+ - alpinion
+size_categories:
+ - n<1K
+---
+
+# USTB - Phantom (Alpinion L3-8)
+
+
+
+*First frame of [`Alpinion_L3-8_CPWC_hypoechoic.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/D_alpinion_phantom/Alpinion_L3-8_CPWC_hypoechoic.hdf5), reconstructed from the raw channel data with `reconstruct.py`.*
+
+## Dataset Description
+
+Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** (see the [collection card](../README.md)).
+
+Phantom channel-capture data acquired on an Alpinion E-Cube 12R research scanner with an L3-8 linear-array probe. Hypoechoic and hyperechoic targets imaged with focused (FI) and coherent plane-wave compounding (CPWC) sequences.
+
+## Dataset Contributor(s)
+
+- Ole Marius Hoel Rindal (primary contact)
+- Yucel Karabiyik
+- Sven Peter Nasholm
+- Andreas Austeng
+- University of Oslo (UiO), Department of Informatics
+
+## Dataset Creation Date
+
+06/23/2026 (packaging date; original acquisitions/simulations were produced between 2016 and 2023).
+
+## License / Terms of Use
+
+[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode.en). Retain attribution and identify modifications when reusing the data.
+
+## Intended Usage
+
+Generalized reconstruction and image-quality assessment on a second hardware platform (RFP task 6.1); cross-vendor robustness studies. (OpenH-RF RFP task 6.1 Generalized Reconstruction).
+
+## Dataset Characterization
+
+- **Data Collection Method:** phantom
+- **Labeling Method:** Derived (known phantom target types).
+- **Acquisition system:** probe(s) L3-8; element positions stored in `/probe/probe_geometry` (meters); center frequency, sampling frequency and sound speed stored per acquisition in `/scan` (see per-sample feature table).
+
+## Processing the Dataset
+
+The example acquisition can be processed with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea).
+
+`zea` streams the data from the Hugging Face Hub and processes it according to the pipeline. You can try it out with the following command:
+
+```bash
+zea process \
+ --dataset hf://nvidia/OpenH-RF/oslo/D_alpinion_phantom/Alpinion_L3-8_CPWC_hypoechoic.hdf5 \
+ --config hf://nvidia/OpenH-RF/oslo/D_alpinion_phantom/pipeline.yaml
+```
+
+This `pipeline.yaml` holds the pipeline and display window of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
+
+## Dataset Format
+
+[zea v0.1.6](https://github.com/tue-bmd/zea)
+
+All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file is a single acquisition with raw channel data `/data/raw_data` of shape `(n_frames, n_tx, n_ax, n_el, n_ch)` and a fully populated `/scan` group describing the transmit sequence (delays, focus distances, steering angles, apodization, timing). Data type: RF (n_ch=1). No demodulation or decimation was applied during packaging beyond conversion from the USTB Ultrasound File Format (UFF) to zea; RF data is demodulated inside the reconstruction pipeline.
+
+## Dataset Quantification
+
+**Current OpenH-RF release:** 4 HDF5 files; 466.42 MB (466,419,712 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+
+- **Number of acquisitions:** 4
+- **Total channel-capture frames:** 4
+- **Train / validation / test split:** not predefined (research dataset).
+- **Stored HDF5 size:** 466.42 MB (466,419,712 bytes).
+
+Per-acquisition summary:
+
+| Acquisition | frames | transmits | samples | elements | n_ch | fs (MHz) | fc (MHz) | size (MB) |
+|---|---|---|---|---|---|---|---|---|
+| `Alpinion_L3-8_CPWC_hyperechoic_scatterers` | 1 | 21 | 4352 | 128 | 1 | 40.0 | 6.00 | 40.89 |
+| `Alpinion_L3-8_CPWC_hypoechoic` | 1 | 21 | 4352 | 128 | 1 | 40.0 | 6.00 | 40.96 |
+| `Alpinion_L3-8_FI_hyperechoic_scatterers` | 1 | 256 | 3474 | 128 | 1 | 40.0 | 6.00 | 192.35 |
+| `Alpinion_L3-8_FI_hypoechoic` | 1 | 256 | 3474 | 128 | 1 | 40.0 | 6.00 | 192.22 |
+
+Per-sample feature table:
+
+| Field | Shape | Dtype | Units | Description |
+|---|---|---|---|---|
+| `data/raw_data` | `(n_frames, n_tx, n_ax, n_el, n_ch)` | float32 | a.u. | Raw pre-beamformed RF channel data |
+| `scan/sampling_frequency` | `scalar` | float32 | Hz | A/D sampling frequency |
+| `scan/center_frequency` | `scalar` | float32 | Hz | Transmit pulse center frequency |
+| `scan/demodulation_frequency` | `scalar` | float32 | Hz | Demodulation (carrier) frequency |
+| `scan/sound_speed` | `scalar` | float32 | m/s | Assumed medium speed of sound |
+| `scan/initial_times` | `(n_tx,)` | float32 | s | A/D start time per transmit |
+| `scan/t0_delays` | `(n_tx, n_el)` | float32 | s | Per-element transmit fire times |
+| `scan/tx_apodizations` | `(n_tx, n_el)` | float32 | - | Per-element transmit apodization |
+| `scan/focus_distances` | `(n_tx,)` | float32 | m | Focus distance per transmit (0 = plane wave) |
+| `scan/polar_angles` | `(n_tx,)` | float32 | rad | Transmit steering (polar) angle |
+| `scan/transmit_origins` | `(n_tx, 3)` | float32 | m | Transmit beam origin (x, y, z) |
+| `probe/probe_geometry` | `(n_el, 3)` | float32 | m | Element positions (x, y, z) |
+
+## Subject Metadata
+
+No human or animal subjects. Targets: hypoechoic/hyperechoic phantom inclusions. Scanner: Alpinion E-Cube 12R. Probe: L3-8 linear array (128 elements).
+
+## Data Validation
+
+A Delay-And-Sum `zea.Pipeline` (`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`; RF is demodulated in-pipeline, IQ uses a baseband pipeline) reconstructs every acquisition as a portable check that the recorded geometry and timing are correct (see *Processing the Dataset*).
+
+The reference B-mode images committed alongside the data (`_bmode.png`) are produced with the UltraSound ToolBox (USTB) MATLAB Delay-And-Sum beamformer — the exact per-dataset reconstruction used in the public USTB dataset catalog (https://unioslo.github.io/USTB/datasets.html), with scanline transmit apodization for focused/sector acquisitions and correct sector-scan geometry. These are the recommended reference reconstructions for visual verification.
+
+## Known Issues
+
+Single-frame acquisitions; two transmit schemes (FI, CPWC).
+
+## Ethical Considerations
+
+Phantom acquisitions; no human or animal subjects. No ethical considerations beyond standard laboratory practice.
+
+## Citation
+
+Please cite the UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo (Zenodo record 20261898).
diff --git a/oslo/D_alpinion_phantom/assets/hero.png b/oslo/D_alpinion_phantom/assets/hero.png
new file mode 100644
index 0000000000000000000000000000000000000000..0d8f234192cd6be2488d585d958771bcf0c40ecf
--- /dev/null
+++ b/oslo/D_alpinion_phantom/assets/hero.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e79bd9ed9833c464ca245d098b1d41c9439e8bb1c632cd40c52e6881ce0c496
+size 338773
diff --git a/jhu-pulselab/alpinion_phantom/assets/main.png b/oslo/D_alpinion_phantom/assets/main.png
similarity index 100%
rename from jhu-pulselab/alpinion_phantom/assets/main.png
rename to oslo/D_alpinion_phantom/assets/main.png
diff --git a/jhu-pulselab/alpinion_phantom/pipeline.yaml b/oslo/D_alpinion_phantom/pipeline.yaml
similarity index 85%
rename from jhu-pulselab/alpinion_phantom/pipeline.yaml
rename to oslo/D_alpinion_phantom/pipeline.yaml
index c970d37726ae6f9586d4c916346d82330730f0be..84214fecf7c2236d3163ee0bc3fc50cf48dab7f6 100644
--- a/jhu-pulselab/alpinion_phantom/pipeline.yaml
+++ b/oslo/D_alpinion_phantom/pipeline.yaml
@@ -1,4 +1,4 @@
-# zea pipeline for alpinion_phantom/JHU_Alpinion_L3-8_CPWC_hypoechoic.hdf5, the example acquisition of this sub-dataset.
+# zea pipeline for D_alpinion_phantom/Alpinion_L3-8_CPWC_hypoechoic.hdf5, the example acquisition of this sub-dataset.
# The shared ../pipeline.yaml plus the per-acquisition settings that
# ../reconstruct.py takes from ../parameters.yaml (display window in metres,
# dynamic range), so `zea process` renders it standalone.
diff --git a/oslo/E_simulation/README.md b/oslo/E_simulation/README.md
index 2b886d87498a752f728f688ab05868fc62953fa8..0d671c2fa1227fbd8f5611046dbbac73d66cb30d 100644
--- a/oslo/E_simulation/README.md
+++ b/oslo/E_simulation/README.md
@@ -9,7 +9,7 @@ language:
tags:
- ultrasound
- rf
- - open-h-rf
+ - openh-rf
- simulation
- field-ii
- synthetic
@@ -21,13 +21,13 @@ size_categories:
-*First frame of [`PICMUS_numerical_calib_v2.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/E_simulation/PICMUS_numerical_calib_v2.hdf5), reconstructed from the raw channel data with `reconstruct.py`.*
+*First frame of [`PICMUS_numerical_calib_v2.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/E_simulation/PICMUS_numerical_calib_v2.hdf5), reconstructed from the raw channel data with `reconstruct.py`.*
## Dataset Description
Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** (see the [collection card](../README.md)).
-Physics-based synthetic channel-capture data generated with the Field II ultrasound simulation framework. The collection covers point scatterers, cysts, speckle, dynamic-range targets and blocked-array (aperture-apodized) configurations, using linear (L7-4-like) and phased (P4-like) virtual probes with CPWC, FI and STA sequences. Because the scattering medium is fully defined, exact ground-truth scatterer positions and medium parameters are known. One numerical calibration acquisition (PICMUS_numerical_calib_v2) was created in collaboration with our group as part of the PICMUS effort; it is included on that basis.
+Physics-based synthetic channel-capture data generated with the Field II ultrasound simulation framework. The collection covers point scatterers, cysts, speckle, dynamic-range targets and blocked-array (aperture-apodized) configurations, using linear (L7-4-like) and phased (P4-like) virtual probes with CPWC, FI and STA sequences. Because the scattering medium is fully defined, exact ground-truth scatterer positions and medium parameters are known. One numerical calibration acquisition (PICMUS_numerical_calib_v2) was created in collaboration with our group as part of the PICMUS effort; it is included here while the other PICMUS datasets are excluded (see Known Issues).
## Dataset Contributor(s)
@@ -47,7 +47,7 @@ Physics-based synthetic channel-capture data generated with the Field II ultraso
## Intended Usage
-Generalized reconstruction, beamformer development and validation with known ground truth (RFP task 6.1); resolution/contrast/dynamic-range characterization; training/validation of learned reconstruction methods. (Open-H-RF RFP task 6.1 Generalized Reconstruction).
+Generalized reconstruction, beamformer development and validation with known ground truth (RFP task 6.1); resolution/contrast/dynamic-range characterization; training/validation of learned reconstruction methods. (OpenH-RF RFP task 6.1 Generalized Reconstruction).
## Dataset Characterization
@@ -63,11 +63,11 @@ The example acquisition can be processed with the `pipeline.yaml` definition in
```bash
zea process \
- --dataset hf://nvidia/Open-H-RF/oslo/E_simulation/PICMUS_numerical_calib_v2.hdf5 \
- --config hf://nvidia/Open-H-RF/oslo/E_simulation/pipeline.yaml
+ --dataset hf://nvidia/OpenH-RF/oslo/E_simulation/PICMUS_numerical_calib_v2.hdf5 \
+ --config hf://nvidia/OpenH-RF/oslo/E_simulation/pipeline.yaml
```
-This `pipeline.yaml` holds the pipeline and display window of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
+This `pipeline.yaml` holds the pipeline and display window of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
## Dataset Format
@@ -77,7 +77,7 @@ All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file i
## Dataset Quantification
-**Current Open-H-RF release:** 11 HDF5 files; 2.98 GB (2,980,642,816 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 11 HDF5 files; 2.98 GB (2,980,642,816 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
- **Number of acquisitions:** 11
- **Total channel-capture frames:** 17
@@ -129,7 +129,7 @@ The reference B-mode images committed alongside the data (`_bmode.png`) ar
## Known Issues
-PICMUS calibration: 'PICMUS_numerical_calib_v2' was created in collaboration with our group as part of the PICMUS (Plane-wave Imaging Challenge in Medical UltraSound, IEEE IUS 2016) effort, and is therefore included here. Simulation framework: Field II (Jensen et al.).
+PICMUS calibration: 'PICMUS_numerical_calib_v2' was created in collaboration with our group as part of the PICMUS (Plane-wave Imaging Challenge in Medical UltraSound, IEEE IUS 2016) effort, and is therefore included here. The other PICMUS acquisitions (in-vivo carotid, experimental and simulated resolution/contrast) are deliberately excluded from this submission. Simulation framework: Field II (Jensen et al.).
## Ethical Considerations
@@ -137,6 +137,4 @@ Fully synthetic data generated with the Field II simulation framework; no human
## Citation
-Please cite the UltraSound ToolBox (USTB) proceedings paper:
-
-A. Rodriguez-Molares, O. M. H. Rindal, O. Bernard, A. Nair, M. A. Lediju Bell, H. Liebgott, A. Austeng and L. Løvstakken, "The UltraSound ToolBox," in *2017 IEEE International Ultrasonics Symposium (IUS)*, 2017, pp. 1–4. doi:[10.1109/ULTSYM.2017.8092389](https://doi.org/10.1109/ULTSYM.2017.8092389)
+Please cite the UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo (Zenodo record 20261898).
diff --git a/oslo/F_motion/README.md b/oslo/F_motion/README.md
index 2c3788f8c977abec150b7439fca6c1c76a64e433..dd2757d8e84dfebd5ffda495013d52bd369285f7 100644
--- a/oslo/F_motion/README.md
+++ b/oslo/F_motion/README.md
@@ -9,7 +9,7 @@ language:
tags:
- ultrasound
- rf
- - open-h-rf
+ - openh-rf
- elastography
- shear-wave
- arfi
@@ -21,7 +21,7 @@ size_categories:
-*First push-track sequence (frames 1-49) of [`SWE_L7_type_III.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/F_motion/SWE_L7_type_III.hdf5): B-mode (left) and axial displacement between consecutive frames (right), a lag-one autocorrelation (Kasai) estimate on the IQ beamformed with `pipeline.yaml`. The displacement estimate is not part of the reference pipeline.*
+*First push-track sequence (frames 1-49) of [`SWE_L7_type_III.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/F_motion/SWE_L7_type_III.hdf5): B-mode (left) and axial displacement between consecutive frames (right), a lag-one autocorrelation (Kasai) estimate on the IQ beamformed with `pipeline.yaml`. The displacement estimate is not part of the reference pipeline.*
## Dataset Description
@@ -47,7 +47,7 @@ Shear-wave elastography (SWE) and acoustic-radiation-force-impulse (ARFI) push-t
## Intended Usage
-Motion estimation (RFP task 6.4): shear-wave velocity estimation, ARFI displacement tracking, and high-frame-rate motion reconstruction. (Open-H-RF RFP task 6.4 Motion Estimation).
+Motion estimation (RFP task 6.4): shear-wave velocity estimation, ARFI displacement tracking, and high-frame-rate motion reconstruction. (OpenH-RF RFP task 6.4 Motion Estimation).
## Dataset Characterization
@@ -63,11 +63,11 @@ The example acquisition can be processed with the `pipeline.yaml` definition in
```bash
zea process \
- --dataset hf://nvidia/Open-H-RF/oslo/F_motion/SWE_L7_type_III.hdf5 \
- --config hf://nvidia/Open-H-RF/oslo/F_motion/pipeline.yaml
+ --dataset hf://nvidia/OpenH-RF/oslo/F_motion/SWE_L7_type_III.hdf5 \
+ --config hf://nvidia/OpenH-RF/oslo/F_motion/pipeline.yaml
```
-This `pipeline.yaml` holds the pipeline and display window of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)). These plane-wave tracking acquisitions use the non-focused `compound` pipeline, and the reference reconstruction shows a single tracking frame.
+This `pipeline.yaml` holds the pipeline and display window of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)). These plane-wave tracking acquisitions use the non-focused `compound` pipeline, and the reference reconstruction shows a single tracking frame.
## Dataset Format
@@ -77,7 +77,7 @@ All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file i
## Dataset Quantification
-**Current Open-H-RF release:** 4 HDF5 files; 354.42 MB (354,418,688 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 4 HDF5 files; 354.42 MB (354,418,688 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
- **Number of acquisitions:** 4
- **Total channel-capture frames:** 800
@@ -130,6 +130,4 @@ Phantom acquisitions; no human or animal subjects. No ethical considerations bey
## Citation
-Please cite the UltraSound ToolBox (USTB) proceedings paper:
-
-A. Rodriguez-Molares, O. M. H. Rindal, O. Bernard, A. Nair, M. A. Lediju Bell, H. Liebgott, A. Austeng and L. Løvstakken, "The UltraSound ToolBox," in *2017 IEEE International Ultrasonics Symposium (IUS)*, 2017, pp. 1–4. doi:[10.1109/ULTSYM.2017.8092389](https://doi.org/10.1109/ULTSYM.2017.8092389)
+Please cite the UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo (Zenodo record 20261898).
diff --git a/oslo/README.md b/oslo/README.md
index 4727a0d8e9d7b72b3bfbc1334d8bcaa660074e48..f034e932588cc6252b15c6537958aa215bd59e47 100644
--- a/oslo/README.md
+++ b/oslo/README.md
@@ -9,7 +9,7 @@ language:
tags:
- ultrasound
- rf
- - open-h-rf
+ - openh-rf
- beamforming
- channel-data
size_categories:
@@ -18,25 +18,26 @@ size_categories:
# UltraSound ToolBox (USTB) Channel Capture Collection
-
+
-*First frames reconstructed from the raw channel data with `reconstruct.py`. Top: [`A_cardiac/Verasonics_P2-4_parasternal_long_subject_1`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/A_cardiac/Verasonics_P2-4_parasternal_long_subject_1.hdf5), [`A_cardiac/Verasonics_P2-4_apical_four_chamber_subject_1`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/A_cardiac/Verasonics_P2-4_apical_four_chamber_subject_1.hdf5), [`C_verasonics_phantom/FI_P4_cysts_center`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/C_verasonics_phantom/FI_P4_cysts_center.hdf5). Bottom: [`B_carotid/L7_FI_carotid_cross_1`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/B_carotid/L7_FI_carotid_cross_1.hdf5), [`C_verasonics_phantom/L7_FI_Verasonics_CIRS`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/C_verasonics_phantom/L7_FI_Verasonics_CIRS.hdf5), [`E_simulation/PICMUS_numerical_calib_v2`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/E_simulation/PICMUS_numerical_calib_v2.hdf5). Panels are scaled to a common height per row, not to a common physical scale.*
+*First frames reconstructed from the raw channel data with `reconstruct.py`. Top: [`A_cardiac/Verasonics_P2-4_parasternal_long_subject_1`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/A_cardiac/Verasonics_P2-4_parasternal_long_subject_1.hdf5), [`A_cardiac/Verasonics_P2-4_apical_four_chamber_subject_1`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/A_cardiac/Verasonics_P2-4_apical_four_chamber_subject_1.hdf5), [`C_verasonics_phantom/FI_P4_cysts_center`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/C_verasonics_phantom/FI_P4_cysts_center.hdf5). Bottom: [`B_carotid/L7_FI_carotid_cross_1`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/B_carotid/L7_FI_carotid_cross_1.hdf5), [`C_verasonics_phantom/L7_FI_Verasonics_CIRS`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/C_verasonics_phantom/L7_FI_Verasonics_CIRS.hdf5), [`D_alpinion_phantom/Alpinion_L3-8_CPWC_hypoechoic`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/D_alpinion_phantom/Alpinion_L3-8_CPWC_hypoechoic.hdf5), [`E_simulation/PICMUS_numerical_calib_v2`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/E_simulation/PICMUS_numerical_calib_v2.hdf5). Panels are scaled to a common height per row, not to a common physical scale.*
## Dataset Description
-Contributed by the **University of Oslo (UiO), Department of Informatics** (the USTB team) to the [Open-H-RF](https://github.com/open-h/Open-H-RF) initiative. All data is pre-beamformed (raw channel-capture) ultrasound stored in the **zea** HDF5 format and released under **CC BY 4.0**.
+Contributed by the **University of Oslo (UiO), Department of Informatics** (the USTB team) to the [OpenH-RF](https://github.com/open-h/OpenH-RF) initiative. All data is pre-beamformed (raw channel-capture) ultrasound stored in the **zea** HDF5 format and released under **CC BY 4.0**.
-35 acquisitions packaged into five application sub-datasets. Each sub-dataset folder contains its zea `.hdf5` files, a Hugging Face–style `README.md` data card, and one reference B-mode PNG per acquisition. The **`reconstruct.py`, `pipeline.yaml`, and the CC BY 4.0 `LICENCE` live once at the submission root** — the reconstruction reconstructs every sub-dataset (the pipeline is identical across folders), and the single LICENCE covers the whole collection (each data card also declares `license: cc-by-4.0` in its YAML frontmatter). See [`jhu-pulselab/alpinion_phantom`](../jhu-pulselab/alpinion_phantom/README.md) to access additional data from this USTB Channel Capture Collection.
+39 acquisitions packaged into six application sub-datasets. Each sub-dataset folder contains its zea `.hdf5` files, a Hugging Face–style `README.md` data card, and one reference B-mode PNG per acquisition. The **`reconstruct.py`, `pipeline.yaml`, and the CC BY 4.0 `LICENCE` live once at the submission root** — the reconstruction reconstructs every sub-dataset (the pipeline is identical across folders), and the single LICENCE covers the whole collection (each data card also declares `license: cc-by-4.0` in its YAML frontmatter).
| Folder | Sub-dataset | Tier | RFP task | Acq. |
|---|---|---|---|---|
| `A_cardiac/` | In-vivo cardiac (Verasonics P4-2) | in-vivo human (research) | 6.1 Generalized Reconstruction | 2 |
| `B_carotid/` | In-vivo carotid (Verasonics L7-4) | in-vivo human (research) | 6.1 Generalized Reconstruction | 3 |
| `C_verasonics_phantom/` | Phantom (Verasonics L7-4 / P4) | phantom | 6.1 Generalized Reconstruction | 15 |
+| `D_alpinion_phantom/` | Phantom (Alpinion L3-8) | phantom | 6.1 Generalized Reconstruction | 4 |
| `E_simulation/` | Simulation (Field II) | simulation | 6.1 Generalized Reconstruction | 11 |
| `F_motion/` | Motion estimation (SWE / ARFI, L7-4) | phantom | 6.4 Motion Estimation | 4 |
-Total: **35 acquisitions**, 8.15 GB of stored HDF5 data.
+Total: **39 acquisitions**, 8.61 GB of stored HDF5 data.
## Dataset Contributor(s)
@@ -56,7 +57,7 @@ Total: **35 acquisitions**, 8.15 GB of stored HDF5 data.
## Processing the Dataset
-Every acquisition is reconstructable from the file alone — all acquisition parameters live in the zea `/scan` and `/probe` groups. The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/oslo/reconstruct.py), as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline*.yaml` and `parameters.yaml` definitions in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub: set `PATHS` to the acquisition to reconstruct (default: the apical four-chamber cardiac scan above) and `REFOCUS = True` to also emit the REFoCUS variant. See [Reconstruction Details](#reconstruction-details) for how each acquisition's pipeline is chosen. Each sub-dataset folder also holds a `pipeline.yaml` for its example acquisition, so it can be rendered with a single `zea process` command (see the sub-dataset cards).
+Every acquisition is reconstructable from the file alone — all acquisition parameters live in the zea `/scan` and `/probe` groups. The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py), as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` and `parameters.yaml` definitions in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub: set `PATHS` to the acquisition to reconstruct (default: the apical four-chamber cardiac scan above) and `REFOCUS = True` to also emit the REFoCUS variant. See [Reconstruction Details](#reconstruction-details) for how each acquisition's pipeline is chosen. Each sub-dataset folder also holds a `pipeline.yaml` for its example acquisition, so it can be rendered with a single `zea process` command (see the sub-dataset cards).
## Dataset Format
@@ -64,7 +65,7 @@ Every acquisition is reconstructable from the file alone — all acquisition par
## Dataset Quantification
-**Current Open-H-RF release:** 35 HDF5 files; 8.15 GB (8,147,173,376 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 39 HDF5 files; 8.61 GB (8,613,593,088 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
## Reconstruction Details
@@ -91,8 +92,4 @@ Each acquisition ships with:
## Citation
-Please cite the UltraSound ToolBox (USTB) proceedings paper:
-
-A. Rodriguez-Molares, O. M. H. Rindal, O. Bernard, A. Nair, M. A. Lediju Bell, H. Liebgott, A. Austeng and L. Løvstakken, "The UltraSound ToolBox," in *2017 IEEE International Ultrasonics Symposium (IUS)*, 2017, pp. 1–4. doi:[10.1109/ULTSYM.2017.8092389](https://doi.org/10.1109/ULTSYM.2017.8092389)
-
-`PICMUS_numerical_calib_v2` (group E) was created **in collaboration with our group** as part of the PICMUS effort and is included on that basis. The seemingly missing group D, the Alpinion phantom data, is now [`jhu-pulselab/alpinion_phantom`](../jhu-pulselab/alpinion_phantom/README.md).
+Please cite the UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo (Zenodo record 20261898). `PICMUS_numerical_calib_v2` (group E) was created **in collaboration with our group** as part of the PICMUS effort and is included on that basis; the other PICMUS datasets are excluded.
diff --git a/oslo/assets/hero.png b/oslo/assets/hero.png
index f3f0f3d4cc52edd11ba144dfaefb0c4af3d9f5b4..4fd9fc95fe0a25e66928e106e59bb4a27fe4244e 100644
--- a/oslo/assets/hero.png
+++ b/oslo/assets/hero.png
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
-oid sha256:01a1a0533e2b29ae770a6333a4136be3a42c01a76c7824de27d5b9df48f784e6
-size 1177139
+oid sha256:1ca2d6006e47c50df7bd972d52ec613fe403bd68de66f2085be72439ae2f1ba4
+size 1522812
diff --git a/oslo/parameters.yaml b/oslo/parameters.yaml
index a3f9899e92091ba2b1c7ff1142fbdb1aed9537bb..3db9c2df0220416b7fc0f75bafb5c6b9d3c78e57 100644
--- a/oslo/parameters.yaml
+++ b/oslo/parameters.yaml
@@ -121,6 +121,29 @@ experimental_dynamic_range_phantom:
zlims: [3, 58]
xlims: [-19, 19]
+# D_alpinion_phantom
+Alpinion_L3-8_CPWC_hyperechoic_scatterers:
+ pipeline: compound
+ refocus: true
+ zlims: [3, 55]
+ xlims: [-19, 19]
+Alpinion_L3-8_CPWC_hypoechoic:
+ pipeline: compound
+ refocus: true
+ zlims: [5, 50]
+ xlims: [-19, 19]
+Alpinion_L3-8_FI_hyperechoic_scatterers:
+ pipeline: scanline
+ refocus: true
+ zlims: [3, 55]
+ xlims: [-19, 19]
+Alpinion_L3-8_FI_hypoechoic:
+ pipeline: scanline
+ refocus: true
+ zlims: [0, 60]
+ xlims: [-19, 19]
+ dynamic_range: [-50, 0]
+
# E_simulation
# Single plane wave (n_tx=1) -> REFoCUS N/A
FieldII_CPWC_point_scatterers_res_v2:
diff --git a/politorino/README.md b/politorino/README.md
index 67a1b53cf4107d5bf0f793c4ff6169923c171de5..5be1344b260466a5a6d5dda9dbbe87acc7e01bd6 100644
--- a/politorino/README.md
+++ b/politorino/README.md
@@ -7,7 +7,7 @@ task_categories:
tags:
- ultrasound
- rf
- - open-h-rf
+ - openh-rf
- medical-imaging
- beamforming
- fascicle tracking
@@ -21,7 +21,7 @@ size_categories:
-*Cine loop of treadmill walking at 2 km/h, [`data/PAT02/PAT02_w1.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/politorino/data/PAT02/PAT02_w1.hdf5), reconstructed from the raw channel data with the `pipeline.yaml` in this folder.*
+*Cine loop of treadmill walking at 2 km/h, [`data/PAT02/PAT02_w1.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/politorino/data/PAT02/PAT02_w1.hdf5), reconstructed from the raw channel data with the `pipeline.yaml` in this folder.*
## Dataset Description
@@ -62,7 +62,7 @@ This data was employed for an initial study evaluating fascicle tracking algorit
## Processing the Dataset
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/politorino/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/politorino/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
The script reconstructs a B-mode from the raw channel data and overlays the stored fascicle tracking on it, writing a `.png`. The constants at the top of the script select what is drawn:
@@ -89,7 +89,7 @@ Per-sample contents of the HDF5:
## Dataset Quantification
-**Current Open-H-RF release:** 30 HDF5 files; 169.95 GB (169,950,314,496 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 30 HDF5 files; 169.95 GB (169,950,314,496 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
- 30 files - 6 acquisitions of 9.6s at 500 fps for each of the 5 subjects
- **Stored HDF5 size:** 169.95 GB (169,950,314,496 bytes); 5.67 GB per file on average. Individual file sizes vary.
diff --git a/resolvestroke/README.md b/resolvestroke/README.md
index 97663742f1f48d8429eae47efc1b3ab95b0e35a4..3aa5ca84a7c8f77707c57cd79b23105710acd27f 100644
--- a/resolvestroke/README.md
+++ b/resolvestroke/README.md
@@ -7,7 +7,7 @@ task_categories:
tags:
- ultrasound
- iq
- - open-h-rf
+ - openh-rf
- 3d
- matrix-probe
- diverging-wave
@@ -26,11 +26,11 @@ size_categories:
-*3D power Doppler of the transcranial CEUS acquisition [`clinical/SP03-Left/SP03-Left.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/resolvestroke/clinical/SP03-Left/SP03-Left.hdf5) at bolus +10 s, x-z maximum-intensity projection, reconstructed from the released channel data.*
+*3D power Doppler of the transcranial CEUS acquisition [`clinical/SP03-Left/SP03-Left.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/resolvestroke/clinical/SP03-Left/SP03-Left.hdf5) at bolus +10 s, x-z maximum-intensity projection, reconstructed from the released channel data.*
## Dataset Description
-[Resolve Stroke](https://www.resolvestroke.com/) develops SYLVER, a software-driven ultrasound platform that supports clinical assessment in patients suspected of, or at risk for, cerebral perfusion abnormalities by improving visualization of cerebral vasculature and providing complementary information on cerebral perfusion. SYLVER images with a 32×32 matrix array probe at 2 MHz using diverging-wave transmits. CE and FDA clearances are expected in 2026. This directory holds the data Resolve Stroke contributes to Open-H-RF: pre-beamformed RF/IQ channel data from an imaging phantom, a flow phantom, and in-vivo transcranial acquisitions, all de-identified and released under CC BY 4.0. The in-vivo data comes from the CPP-approved SCULPT clinical study. Two transmit sequences are included: a saddle sequence that images a 2D plane for real-time B-mode, and a 4 kHz single-aperture volume sequence for CEUS and blood-flow measurement.
+[Resolve Stroke](https://www.resolvestroke.com/) develops SYLVER, a software-driven ultrasound platform that supports clinical assessment in patients suspected of, or at risk for, cerebral perfusion abnormalities by improving visualization of cerebral vasculature and providing complementary information on cerebral perfusion. SYLVER images with a 32×32 matrix array probe at 2 MHz using diverging-wave transmits. CE and FDA clearances are expected in 2026. This directory holds the data Resolve Stroke contributes to OpenH-RF: pre-beamformed RF/IQ channel data from an imaging phantom, a flow phantom, and in-vivo transcranial acquisitions, all de-identified and released under CC BY 4.0. The in-vivo data comes from the CPP-approved SCULPT clinical study. Two transmit sequences are included: a saddle sequence that images a 2D plane for real-time B-mode, and a 4 kHz single-aperture volume sequence for CEUS and blood-flow measurement.
Each dataset directory holds its data card (`README.md`), a `reconstruct.py` + `pipeline.yaml` beamforming recipe and, for the CEUS datasets, a power-Doppler reconstruction; the HDF5 files are on the Hub and the preview images in [`assets/`](assets/).
@@ -53,7 +53,7 @@ Each dataset directory holds its data card (`README.md`), a `reconstruct.py` + `
## Processing the Dataset
-Each sub-dataset has its own `reconstruct.py` and `pipeline.yaml`, as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF/tree/main/datasets/resolvestroke), built on the [zea library](https://github.com/tue-bmd/zea); the CEUS datasets add a power-Doppler reconstruction. See the data card of each sub-dataset for how to run them.
+Each sub-dataset has its own `reconstruct.py` and `pipeline.yaml`, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF/tree/main/datasets/resolvestroke), built on the [zea library](https://github.com/tue-bmd/zea); the CEUS datasets add a power-Doppler reconstruction. See the data card of each sub-dataset for how to run them.
The Python scripts carry their own `SPDX-License-Identifier: Apache-2.0` header; the dataset itself is CC BY 4.0.
@@ -63,7 +63,7 @@ The Python scripts carry their own `SPDX-License-Identifier: Apache-2.0` header;
## Dataset Quantification
-**Current Open-H-RF release:** 43 HDF5 files; 110.62 GB (110,619,394,048 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 43 HDF5 files; 110.62 GB (110,619,394,048 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
## Contents
diff --git a/resolvestroke/clinical/README.md b/resolvestroke/clinical/README.md
index 2690be3e43335c07a099ae0d439557f1fbf7d3c3..0b24417ffa4dbf4963c43e5708bb471beb0b3c90 100644
--- a/resolvestroke/clinical/README.md
+++ b/resolvestroke/clinical/README.md
@@ -7,7 +7,7 @@ task_categories:
tags:
- ultrasound
- iq
- - open-h-rf
+ - openh-rf
- 3d
- matrix-probe
- contrast-enhanced
@@ -24,7 +24,7 @@ size_categories:
-*Reference microvascular image (`mvi`, x-z maximum-intensity projection of the computed reference) for the 20 acquisitions in [`clinical/`](https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical).*
+*Reference microvascular image (`mvi`, x-z maximum-intensity projection of the computed reference) for the 20 acquisitions in [`clinical/`](https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical).*
## Dataset Description
@@ -71,12 +71,12 @@ Contrast-enhanced ultrasound flow/perfusion quantification, microbubble detectio
## Processing the Dataset
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/resolvestroke/clinical/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/resolvestroke/clinical/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
-The reconstruction recipe is identical for all 20 acquisitions, so the scripts and pipeline YAMLs live once in this directory. Each script has a `SUBJECT` constant at the top (default `"SP02-Left-2"`); set it to any acquisition name from the inventory, e.g. `SUBJECT = "SP07-Right"`, and the `hf://` input path `hf://nvidia/Open-H-RF/resolvestroke/clinical//.hdf5` and the output filenames follow from it. Swap `ZEA_FILE` (`INPUT` in `reconstruct_PD_3d.py`) for a local path to run against your own copy.
+The reconstruction recipe is identical for all 20 acquisitions, so the scripts and pipeline YAMLs live once in this directory. Each script has a `SUBJECT` constant at the top (default `"SP02-Left-2"`); set it to any acquisition name from the inventory, e.g. `SUBJECT = "SP07-Right"`, and the `hf://` input path `hf://nvidia/OpenH-RF/resolvestroke/clinical//.hdf5` and the output filenames follow from it. Swap `ZEA_FILE` (`INPUT` in `reconstruct_PD_3d.py`) for a local path to run against your own copy.
```bash
-uv run --project /path/to/Open-H-RF python reconstruct.py
+uv run --project /path/to/OpenH-RF python reconstruct.py
```
`reconstruct_PD_3d.py` (with `pipeline_PD_3d.yaml`) renders the power-Doppler reconstruction and `display_references.py` the computed reference maps; both are described below.
@@ -95,7 +95,7 @@ The files are in the zea HDF5 format, root `zea_version` 0.1.6. `data/raw_data`
## Dataset Quantification
-**Current Open-H-RF release:** 20 HDF5 files; 105.16 GB (105,159,196,672 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 20 HDF5 files; 105.16 GB (105,159,196,672 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
- Acquisitions: 20 (10 subjects × 2)
- Frames per acquisition: 20000 (5 clips × 4000 frames)
@@ -129,26 +129,26 @@ One row per acquisition. Power Doppler: x-z maximum-intensity projection of each
Acquisition
Subject
Side
Power Doppler, five clips (x-z MIP)
Reference maps (mvi · velocity_radial_avg, x-z and y-z)
## Subject Metadata
@@ -177,7 +177,7 @@ A display-only linear TGC (1.5 dB/cm) adds a depth-dependent dB gain to the rend
Run (a GPU is strongly recommended: about 14 min per acquisition with `uv sync --extra gpu`, hours with the CPU-only JAX of the plain `uv sync`):
```bash
-uv run --project /path/to/Open-H-RF python reconstruct_PD_3d.py
+uv run --project /path/to/OpenH-RF python reconstruct_PD_3d.py
```
## Computed References
@@ -204,7 +204,7 @@ Notes:
```bash
-uv run --project /path/to/Open-H-RF python display_references.py
+uv run --project /path/to/OpenH-RF python display_references.py
```
## Ethical Considerations
diff --git a/resolvestroke/phantom_flow/README.md b/resolvestroke/phantom_flow/README.md
index cd025e6e60357b84591b622e20f1bb6188752962..2862eb8349ca3dbadb8ba9c4da4fcd2e47f8af3f 100644
--- a/resolvestroke/phantom_flow/README.md
+++ b/resolvestroke/phantom_flow/README.md
@@ -7,7 +7,7 @@ task_categories:
tags:
- ultrasound
- iq
- - open-h-rf
+ - openh-rf
- 3d
- matrix-probe
- contrast-enhanced
@@ -23,7 +23,7 @@ size_categories:
-*Power Doppler of the five clips (x-z and y-z maximum-intensity projections), with the reference `mvi` in the last column, reconstructed from [`phantom_flow.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/resolvestroke/phantom_flow/phantom_flow.hdf5).*
+*Power Doppler of the five clips (x-z and y-z maximum-intensity projections), with the reference `mvi` in the last column, reconstructed from [`phantom_flow.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/resolvestroke/phantom_flow/phantom_flow.hdf5).*
## Dataset Description
@@ -68,10 +68,10 @@ Contrast-enhanced ultrasound flow quantification, microbubble detection and trac
## Processing the Dataset
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/resolvestroke/phantom_flow/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/resolvestroke/phantom_flow/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
```bash
-uv run --project /path/to/Open-H-RF python reconstruct.py
+uv run --project /path/to/OpenH-RF python reconstruct.py
```
`reconstruct_PD_3d.py` (with `pipeline_PD_3d.yaml`) renders the power-Doppler reconstruction and `display_references.py` the computed reference maps; both are described below.
@@ -90,7 +90,7 @@ The file is in the zea HDF5 format, root `zea_version` 0.1.6, validated `complia
## Dataset Quantification
-**Current Open-H-RF release:** 1 HDF5 file; 5.25 GB (5,250,744,320 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 1 HDF5 file; 5.25 GB (5,250,744,320 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
- Frames: 20000 (5 clips × 4000 frames)
- Frame rate: 4000 Hz (within each clip)
@@ -136,7 +136,7 @@ The resulting montage is shown at the top of this card.
Run (a GPU is strongly recommended: about 14 min per acquisition with `uv sync --extra gpu`, hours with the CPU-only JAX of the plain `uv sync`):
```bash
-uv run --project /path/to/Open-H-RF python reconstruct_PD_3d.py
+uv run --project /path/to/OpenH-RF python reconstruct_PD_3d.py
```
## Computed References
@@ -162,7 +162,7 @@ Notes:
-Run: `uv run --project /path/to/Open-H-RF python display_references.py`
+Run: `uv run --project /path/to/OpenH-RF python display_references.py`
## Known Issues
diff --git a/resolvestroke/phantom_mp/README.md b/resolvestroke/phantom_mp/README.md
index 54b931b167112f55103959bff79706cc246cc832..23aa4fc8128c3bbbb13cfe32c765f546178ee31d 100644
--- a/resolvestroke/phantom_mp/README.md
+++ b/resolvestroke/phantom_mp/README.md
@@ -7,7 +7,7 @@ task_categories:
tags:
- ultrasound
- iq
- - open-h-rf
+ - openh-rf
- 3d
- matrix-probe
- phantom
@@ -21,7 +21,7 @@ size_categories:
-*B-mode of one frame, x-z and y-z sectors, reconstructed from [`phantom_mp.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/resolvestroke/phantom_mp/phantom_mp.hdf5).*
+*B-mode of one frame, x-z and y-z sectors, reconstructed from [`phantom_mp.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/resolvestroke/phantom_mp/phantom_mp.hdf5).*
## Dataset Description
@@ -56,10 +56,10 @@ Beamforming and reconstruction research (RFP task group 6.1, Generalized Reconst
## Processing the Dataset
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/resolvestroke/phantom_mp/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/resolvestroke/phantom_mp/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
```bash
-uv run --project /path/to/Open-H-RF python reconstruct.py
+uv run --project /path/to/OpenH-RF python reconstruct.py
```
The Python scripts carry their own `SPDX-License-Identifier: Apache-2.0` header; the dataset itself is CC BY 4.0.
@@ -72,7 +72,7 @@ Single zea HDF5 file (`phantom_mp.hdf5`), one track holding the raw channel data
## Dataset Quantification
-**Current Open-H-RF release:** 1 HDF5 file; 33.75 MB (33,751,040 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 1 HDF5 file; 33.75 MB (33,751,040 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
- Frames: 1000 (single acquisition)
- Train / val / test split: N/A
diff --git a/resolvestroke/saddle/README.md b/resolvestroke/saddle/README.md
index 375156f06b301f508b803f1607ec9795d7be8c86..2b8b9b42c74b8ddeae85ba3c65943a614a5a2c75 100644
--- a/resolvestroke/saddle/README.md
+++ b/resolvestroke/saddle/README.md
@@ -7,7 +7,7 @@ task_categories:
tags:
- ultrasound
- iq
- - open-h-rf
+ - openh-rf
- matrix-probe
- diverging-wave
- b-mode
@@ -24,13 +24,13 @@ size_categories:
-*Saddle-array B-modes of all 21 files in [`saddle/data/`](https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/saddle/data), one panel per dataset; the phantom (`PMP01`) shows a regular column of point targets.*
+*Saddle-array B-modes of all 21 files in [`saddle/data/`](https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/saddle/data), one panel per dataset; the phantom (`PMP01`) shows a regular column of point targets.*
## Dataset Description
Single-frame anatomical B-mode acquisitions from the "saddle" imaging sequence of Resolve Stroke's SYLVER ultrasound device, using a 32×32 matrix probe. Each raw acquisition contains, alongside the multi-thousand-frame contrast-enhanced ultrasound (CEUS) sequence, one wide-angle diverging-wave frame (the *saddle* sequence: `x_ang` −24°…+24° in nine steps, no elevation steering, cylindrical elevation focus) received on the full 1024-element aperture through four consecutive 256-element receive events per transmit. Beamformed, this single frame gives a sector B-mode of the imaging plane: the structural reference view acquired at the same probe placement as the contrast (CEUS) recording.
-This directory contains one such reference B-mode per dataset: 20 clinical transcranial acquisitions (SCULPT study) and 1 static matrix-probe imaging phantom (21 files total). It is the structural companion to the Open-H-RF Resolve Stroke clinical CEUS clip submission, and the two use the same anonymized subject codes.
+This directory contains one such reference B-mode per dataset: 20 clinical transcranial acquisitions (SCULPT study) and 1 static matrix-probe imaging phantom (21 files total). It is the structural companion to the OpenH-RF Resolve Stroke clinical CEUS clip submission, and the two use the same anonymized subject codes.
## Dataset Contributor(s)
@@ -51,7 +51,7 @@ This directory contains one such reference B-mode per dataset: 20 clinical trans
## Intended Usage
-Matrix-probe diverging-wave beamforming research, anatomical B-mode reconstruction, and structural reference for the companion transcranial CEUS flow/perfusion datasets (Open-H-RF request-for-proposals task group 6.2, Blood Flow).
+Matrix-probe diverging-wave beamforming research, anatomical B-mode reconstruction, and structural reference for the companion transcranial CEUS flow/perfusion datasets (OpenH-RF request-for-proposals task group 6.2, Blood Flow).
## Dataset Characterization
@@ -61,10 +61,10 @@ Matrix-probe diverging-wave beamforming research, anatomical B-mode reconstructi
## Processing the Dataset
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/resolvestroke/saddle/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/resolvestroke/saddle/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
```bash
-uv run --project /path/to/Open-H-RF python reconstruct.py
+uv run --project /path/to/OpenH-RF python reconstruct.py
```
`reconstruct.py` streams `PMP01.hdf5` from the Hub by default; set `ZEA_FILE` at the top of the script to another of the 21 files (or a local path). The B-mode PNG is written to `assets/_bmode.png`.
@@ -83,7 +83,7 @@ The hardware time-gain compensation is baked into `raw_data`; `scan/tgc_gain_cur
## Dataset Quantification
-**Current Open-H-RF release:** 21 HDF5 files; 175.70 MB (175,702,016 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 21 HDF5 files; 175.70 MB (175,702,016 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
- Files: 21 (20 clinical + 1 phantom)
- Frames per file: 1
diff --git a/siemens-healthineers/README.md b/siemens-healthineers/README.md
index 106430c0437227a7b2fd5c5f09601fd95e4c0d87..ab47e6f973d121c12e844e1a756652a64cd3a508 100644
--- a/siemens-healthineers/README.md
+++ b/siemens-healthineers/README.md
@@ -7,7 +7,7 @@ task_categories:
tags:
- ultrasound
- iq
- - open-h-rf
+ - openh-rf
- medical-imaging
- beamforming
- in-vivo
@@ -21,7 +21,7 @@ size_categories:
-*Four of the anatomies in this dataset, each beamformed from `data/raw_data`: [`Subject_03_acq_018`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/siemens-healthineers/data/Subject_03_acq_018.hdf5), [`Subject_02_acq_004`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/siemens-healthineers/data/Subject_02_acq_004.hdf5), [`Subject_03_acq_008`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/siemens-healthineers/data/Subject_03_acq_008.hdf5), [`Subject_05_acq_031`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/siemens-healthineers/data/Subject_05_acq_031.hdf5).*
+*Four of the anatomies in this dataset, each beamformed from `data/raw_data`: [`Subject_03_acq_018`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/siemens-healthineers/data/Subject_03_acq_018.hdf5), [`Subject_02_acq_004`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/siemens-healthineers/data/Subject_02_acq_004.hdf5), [`Subject_03_acq_008`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/siemens-healthineers/data/Subject_03_acq_008.hdf5), [`Subject_05_acq_031`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/siemens-healthineers/data/Subject_05_acq_031.hdf5).*
## Dataset Description
@@ -61,11 +61,11 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
```bash
zea process \
- --dataset hf://nvidia/Open-H-RF/siemens-healthineers/data/Subject_03_acq_008.hdf5 \
- --config hf://nvidia/Open-H-RF/siemens-healthineers/pipeline.yaml
+ --dataset hf://nvidia/OpenH-RF/siemens-healthineers/data/Subject_03_acq_008.hdf5 \
+ --config hf://nvidia/OpenH-RF/siemens-healthineers/pipeline.yaml
```
-Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/siemens-healthineers/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF).
+Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/siemens-healthineers/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF).
## Dataset Format
@@ -105,7 +105,7 @@ All spatial maps carry per-pixel Cartesian `coordinates` in meters, last axis `[
## Dataset Quantification
-**Current Open-H-RF release:** 590 HDF5 files; 228.54 GB (228,538,449,920 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 590 HDF5 files; 228.54 GB (228,538,449,920 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
- **590 acquisitions (4,539 frames) from 15 subjects**, one continuous dataset: 51 acquisitions / 411 frames (Subjects 01–03) from the first acquisition round and 539 acquisitions / 4,128 frames (Subjects 04–15) from the second.
- **File naming:** one HDF5 per acquisition, `Subject_XX_acq_NNN.hdf5` (acquisitions numbered per subject in acquisition order).
diff --git a/stanford-murine/README.md b/stanford-murine/README.md
index 572b22b00fcbb32413132b4e2a42b80a9ebd31db..2da42922ec058d5afce6e71a95505d3dfc50abcc 100644
--- a/stanford-murine/README.md
+++ b/stanford-murine/README.md
@@ -8,7 +8,7 @@ task_categories:
tags:
- ultrasound
- rf
-- open-h-rf
+- openh-rf
- beamforming
- sound-speed-estimation
- aberration-correction
@@ -25,7 +25,7 @@ size_categories:
-*Frame 0 reconstructed with `reconstruct.py` (L12-3v, 60 dB). Top: the exposed liver of [Rat3](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/stanford-murine/data/RatExperiments/VerasonicsAcq/Rat3/ExposedLiver/DATA_Tracks_20190319_115804.hdf5), `multifocal` track. Bottom: the ATS 549 phantom's [lesions](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/stanford-murine/data/SoSExperiments/august13_2020/L12_3v/ATS_phantom/lesion/DATA_Tracks_20200813_164505.hdf5) and [point targets](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/stanford-murine/data/SoSExperiments/august13_2020/L12_3v/ATS_phantom/point_target/DATA_Tracks_20200813_164745.hdf5), `hadamard` track. The point targets curve into arcs because the phantom is beamformed at 1540 m/s while its own sound speed is lower — the mismatch this dataset is built to estimate and correct.*
+*Frame 0 reconstructed with `reconstruct.py` (L12-3v, 60 dB). Top: the exposed liver of [Rat3](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/stanford-murine/data/RatExperiments/VerasonicsAcq/Rat3/ExposedLiver/DATA_Tracks_20190319_115804.hdf5), `multifocal` track. Bottom: the ATS 549 phantom's [lesions](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/stanford-murine/data/SoSExperiments/august13_2020/L12_3v/ATS_phantom/lesion/DATA_Tracks_20200813_164505.hdf5) and [point targets](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/stanford-murine/data/SoSExperiments/august13_2020/L12_3v/ATS_phantom/point_target/DATA_Tracks_20200813_164745.hdf5), `hadamard` track. The point targets curve into arcs because the phantom is beamformed at 1540 m/s while its own sound speed is lower — the mismatch this dataset is built to estimate and correct.*
## Dataset Description
@@ -38,7 +38,7 @@ The source data are available from Figshare:
The original Figshare releases were published under Apache 2.0; this converted release is CC BY 4.0.
-The converted dataset is hosted at [nvidia/Open-H-RF/stanford-murine](https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/stanford-murine).
+The converted dataset is hosted at [nvidia/OpenH-RF/stanford-murine](https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/stanford-murine).
## Dataset Contributor(s)
@@ -109,13 +109,13 @@ The acquisitions can be processed with the `pipeline_multifocal.yaml`, `pipeline
```bash
zea process \
- --dataset hf://nvidia/Open-H-RF/stanford-murine/data/RatExperiments/VerasonicsAcq/Rat3/ExposedLiver/DATA_Tracks_20190319_115804.hdf5 \
- --config hf://nvidia/Open-H-RF/stanford-murine/pipeline_synthetic_aperture.yaml \
+ --dataset hf://nvidia/OpenH-RF/stanford-murine/data/RatExperiments/VerasonicsAcq/Rat3/ExposedLiver/DATA_Tracks_20190319_115804.hdf5 \
+ --config hf://nvidia/OpenH-RF/stanford-murine/pipeline_synthetic_aperture.yaml \
--track synthetic_aperture \
--n-frames 1
```
-Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/stanford-murine/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF). It reconstructs the first frame of every track in `ZEA_FILE` with the same pipelines and writes one image per track to `assets/`. On top of the YAML, the script inserts a baseband FIR low-pass after demodulation, with its cutoff at half the probe bandwidth (4.0 MHz for L12-3v, 3.0 MHz for L12-5, 1.3 MHz for C5-2v). The cutoff depends on the probe, so it is not part of the pipeline YAMLs and `zea process` runs without it; the difference is small on the rat acquisitions and clearly visible at depth on the phantoms.
+Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/stanford-murine/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF). It reconstructs the first frame of every track in `ZEA_FILE` with the same pipelines and writes one image per track to `assets/`. On top of the YAML, the script inserts a baseband FIR low-pass after demodulation, with its cutoff at half the probe bandwidth (4.0 MHz for L12-3v, 3.0 MHz for L12-5, 1.3 MHz for C5-2v). The cutoff depends on the probe, so it is not part of the pipeline YAMLs and `zea process` runs without it; the difference is small on the rat acquisitions and clearly visible at depth on the phantoms.
## Dataset Format
@@ -141,7 +141,7 @@ Reconstructed B-mode images and segmentation masks are not stored in the convert
## Dataset Quantification
-**Current Open-H-RF release:** 245 HDF5 files; 328.99 GB (328,985,804,800 bytes) stored; root `zea_version` **0.1.4**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 245 HDF5 files; 328.99 GB (328,985,804,800 bytes) stored; root `zea_version` **0.1.4**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
The original four-file demo (not the complete HF release summarized above) contains four HDF5 acquisition bundles: one Rat9 bundle and one phantom bundle for each of C5-2v, L12-3v, and L12-5. Each bundle has three sequence tracks, and each track retains every frame in its source MAT file. That demo has 12 tracks and 64 frames and occupies approximately 5.5 GiB; HDF5 writer and compression versions may change the exact byte count.
diff --git a/strasbourg-basel/README.md b/strasbourg-basel/README.md
index a3929405b48c07d595576ccf7024eac2ef1ec5cf..705f83937b077f08e6c8411b2db9db6e76a9b92d 100644
--- a/strasbourg-basel/README.md
+++ b/strasbourg-basel/README.md
@@ -7,7 +7,7 @@ task_categories:
tags:
- ultrasound
- rf
- - open-h-rf
+ - openh-rf
- tracked-ultrasound
- robot-tracked
- probe-pose
@@ -29,7 +29,7 @@ size_categories:
-*B-mode of a fractured-femur phantom, frame 45 of [`data/phantom2_wholebone.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/strasbourg-basel/BoneSRF/data/phantom2_wholebone.hdf5), beamformed from the recovered channel data.*
+*B-mode of a fractured-femur phantom, frame 45 of [`data/phantom2_wholebone.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/strasbourg-basel/BoneSRF/data/phantom2_wholebone.hdf5), beamformed from the recovered channel data.*
## Dataset Description
@@ -37,7 +37,7 @@ size_categories:
-**BoneSRF** (Bone Surface Reflection) is an ultrasound channel-data dataset for bone-surface and fracture-reflection research. It contains three 3D-printed, fractured femur phantoms, scanned with a handheld point-of-care probe, inverted back to pre-beamformed RF and packaged in the Open-H-RF `zea` format.
+**BoneSRF** (Bone Surface Reflection) is an ultrasound channel-data dataset for bone-surface and fracture-reflection research. It contains three 3D-printed, fractured femur phantoms, scanned with a handheld point-of-care probe, inverted back to pre-beamformed RF and packaged in the OpenH-RF `zea` format.
Each phantom was swept three times (`distal`, `proximal`, `wholebone`), giving nine `zea` HDF5 files. Every scan is robot-tracked: the probe was mounted on a robotic arm and its pose recorded separately. Each file also carries that phantom's CT scan and multi-label segmentation.
@@ -49,7 +49,7 @@ This is phantom data, not simulated, clinical or in-vivo. It is intended for ful
1. **Acquisition.** Each phantom was scanned with a Clarius L20HD3 linear array, producing the probe's own beamformed RF output. This is the physically acquired data, not simulated.
2. **Inversion.** The beamformed RF is inverted back into pre-beamformed, per-element channel data with [`das-inverse` (`clarius` branch)](https://github.com/sankethvedula/das-inverse/tree/clarius), a CGLS solver over a `zea.inverse.DASOperator` forward model of the probe's focused, walking-sub-aperture transmit sequence (`invert_clarius_beamformed.py`). The result is not sensor-captured, but numerically consistent with the real beamformed acquisition it was inverted from.
-3. **Packaging.** The inverted channel data, transmit-sequence metadata, probe geometry, per-frame probe pose, and the phantom's CT + segmentation are written out as one `zea` HDF5 file per scan, matching the Open-H-RF format spec.
+3. **Packaging.** The inverted channel data, transmit-sequence metadata, probe geometry, per-frame probe pose, and the phantom's CT + segmentation are written out as one `zea` HDF5 file per scan, matching the OpenH-RF format spec.
### Probe tracking
@@ -91,7 +91,7 @@ The CT and segmentation data inside the files are released under the same CC BY
## Processing the Dataset
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/strasbourg-basel/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/strasbourg-basel/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
The script loads the saved `zea.Pipeline` from `pipeline.yaml`, runs it on one frame of one scan, and writes `assets/.png`.
@@ -105,7 +105,7 @@ The constants at the top of the script select what is reconstructed:
These values reconstruct `phantom2_wholebone` at its reference frame (45) on the CPU:
```python
-SCAN = "hf://nvidia/Open-H-RF/strasbourg-basel/BoneSRF/data/phantom2_wholebone.hdf5"
+SCAN = "hf://nvidia/OpenH-RF/strasbourg-basel/BoneSRF/data/phantom2_wholebone.hdf5"
FRAME = None
DEVICE = "cpu"
```
@@ -226,7 +226,7 @@ The CT is reference imaging of the physical phantom in scanner (LPS) space. It i
## Dataset Quantification
-**Current Open-H-RF release:** 9 HDF5 files; 200.75 GB (200,745,025,536 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 9 HDF5 files; 200.75 GB (200,745,025,536 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
Nine acquisitions, one continuous sweep each; 1,427 frames in total. No train / val / test split (each file is a single reference acquisition). Every scan has one tracked probe pose per frame.
diff --git a/technion/bladder/README.md b/technion/bladder/README.md
index 4d144d8418752776712d50bf1d9cfb6562355299..d36f9ec1f98c8f64ef498e4742e61dee6a79e29f 100644
--- a/technion/bladder/README.md
+++ b/technion/bladder/README.md
@@ -7,7 +7,7 @@ task_categories:
tags:
- ultrasound
- iq
- - open-h-rf
+ - openh-rf
- beamforming
- bladder
- 3d
@@ -21,7 +21,7 @@ size_categories:
-*Transverse suprapubic view: one cine loop (10 frames) from [`data/a1.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/technion/bladder/data/a1.hdf5).*
+*Transverse suprapubic view: one cine loop (10 frames) from [`data/a1.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/technion/bladder/data/a1.hdf5).*
## Dataset Description
@@ -37,7 +37,7 @@ Real, **in-vivo human** pre-beamformed ultrasound **channel data** for bladder i
## Dataset Creation Date
-Source data 2018; converted to the Open-H-RF (zea) format 07/16/2026.
+Source data 2018; converted to the OpenH-RF (zea) format 07/16/2026.
## License / Terms of Use
@@ -61,12 +61,12 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
```bash
zea process \
- --dataset hf://nvidia/Open-H-RF/technion/bladder/data/a1.hdf5 \
- --config hf://nvidia/Open-H-RF/technion/bladder/pipeline.yaml \
+ --dataset hf://nvidia/OpenH-RF/technion/bladder/data/a1.hdf5 \
+ --config hf://nvidia/OpenH-RF/technion/bladder/pipeline.yaml \
--n-frames 10
```
-Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/technion/bladder/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF).
+Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/technion/bladder/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF).
## Dataset Format
@@ -76,7 +76,7 @@ zea file format, one HDF5 file per sweep (`data/.hdf5`, e.g. `a1.hdf5`,
## Dataset Quantification
-**Current Open-H-RF release:** 14 HDF5 files; 83.71 GB (83,713,785,856 bytes) stored; root `zea_version` **0.1.4**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 14 HDF5 files; 83.71 GB (83,713,785,856 bytes) stored; root `zea_version` **0.1.4**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
- **Frames / sweeps / subjects:** 1,508 frames · 14 sweeps · 7 subjects.
- **Train / val / test split:** N/A (contributor to define).
diff --git a/technion/cardiac/README.md b/technion/cardiac/README.md
index e82e2b21711f0bea44379c3774828f46a21fc8fb..1af40efd4833ef030d801d3ece9bd7a2eb86e423 100644
--- a/technion/cardiac/README.md
+++ b/technion/cardiac/README.md
@@ -7,7 +7,7 @@ task_categories:
tags:
- ultrasound
- iq
- - open-h-rf
+ - openh-rf
- beamforming
- cardiac
- 3d
@@ -21,7 +21,7 @@ size_categories:
-*Apical four-chamber view: one cine loop (32 frames) from [`data/c1.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/technion/cardiac/data/c1.hdf5).*
+*Apical four-chamber view: one cine loop (32 frames) from [`data/c1.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/technion/cardiac/data/c1.hdf5).*
## Dataset Description
@@ -37,7 +37,7 @@ Real, **in-vivo human** pre-beamformed ultrasound **channel data** for cardiac i
## Dataset Creation Date
-Acquired 2018; converted to the Open-H-RF (zea) format 07/16/2026.
+Acquired 2018; converted to the OpenH-RF (zea) format 07/16/2026.
## License / Terms of Use
@@ -61,12 +61,12 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
```bash
zea process \
- --dataset hf://nvidia/Open-H-RF/technion/cardiac/data/c1.hdf5 \
- --config hf://nvidia/Open-H-RF/technion/cardiac/pipeline.yaml \
+ --dataset hf://nvidia/OpenH-RF/technion/cardiac/data/c1.hdf5 \
+ --config hf://nvidia/OpenH-RF/technion/cardiac/pipeline.yaml \
--n-frames 32
```
-Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/technion/cardiac/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF).
+Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/technion/cardiac/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF).
## Dataset Format
@@ -76,7 +76,7 @@ zea file format, one HDF5 file per cine loop (`data/.hdf5`, e.g.
## Dataset Quantification
-**Current Open-H-RF release:** 25 HDF5 files; 14.99 GB (14,992,998,400 bytes) stored; root `zea_version` **0.1.4**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 25 HDF5 files; 14.99 GB (14,992,998,400 bytes) stored; root `zea_version` **0.1.4**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
- **Frames / cines / subjects:** 777 frames · 25 cine loops · 6 subjects (a–f).
- **Train / val / test split:** N/A (contributor to define; the `f2` patient set is a natural held-out cine).
diff --git a/technion/phantom/README.md b/technion/phantom/README.md
index 5e38ac92b7513c1ebc3efe83c3c3ac7c123bdb17..af779c2247b61bd067bc9bd2d8262d5241291a4a 100644
--- a/technion/phantom/README.md
+++ b/technion/phantom/README.md
@@ -7,7 +7,7 @@ task_categories:
tags:
- ultrasound
- iq
- - open-h-rf
+ - openh-rf
- beamforming
- phantom
- 3d
@@ -21,7 +21,7 @@ size_categories:
-*Frame 6 of [`data/ph.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/technion/phantom/data/ph.hdf5), reconstructed by `reconstruct.py`.*
+*Frame 6 of [`data/ph.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/technion/phantom/data/ph.hdf5), reconstructed by `reconstruct.py`.*
## Dataset Description
@@ -37,7 +37,7 @@ Pre-beamformed ultrasound **channel data** from a tissue-mimicking phantom, acqu
## Dataset Creation Date
-Source data 2018; converted to the Open-H-RF (zea) format 07/16/2026.
+Source data 2018; converted to the OpenH-RF (zea) format 07/16/2026.
## License / Terms of Use
@@ -61,12 +61,12 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
```bash
zea process \
- --dataset hf://nvidia/Open-H-RF/technion/phantom/data/ph.hdf5 \
- --config hf://nvidia/Open-H-RF/technion/phantom/pipeline.yaml \
+ --dataset hf://nvidia/OpenH-RF/technion/phantom/data/ph.hdf5 \
+ --config hf://nvidia/OpenH-RF/technion/phantom/pipeline.yaml \
--n-frames 1
```
-Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/technion/phantom/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF).
+Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/technion/phantom/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF).
## Dataset Format
@@ -76,7 +76,7 @@ zea file format, a single HDF5 file `data/ph.hdf5`. Source complex samples repac
## Dataset Quantification
-**Current Open-H-RF release:** 1 HDF5 file; 659.95 MB (659,947,520 bytes) stored; root `zea_version` **0.1.4**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 1 HDF5 file; 659.95 MB (659,947,520 bytes) stored; root `zea_version` **0.1.4**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
- **Frames / acquisitions:** 12 frames · 1 acquisition.
- **Stored HDF5 size:** 659.95 MB (659,947,520 bytes).
diff --git a/tel-aviv/README.md b/tel-aviv/README.md
index 503ad80e7756656abcaf2dc709f015bd356f9e47..af6f550fd74fe744fee02f05a81bbf07359a58ed 100644
--- a/tel-aviv/README.md
+++ b/tel-aviv/README.md
@@ -3,14 +3,14 @@ name: tel-aviv
license: cc-by-4.0
pretty_name: Ilovitsh Lab Mice Tumors
task_categories: [image-segmentation]
-tags: [ultrasound, rf, open-h-rf]
+tags: [ultrasound, rf, openh-rf]
---
# Ilovitsh Lab Mice Tumors & Water-Bead Phantoms
-*Cine loop of the rotational sweep through a mouse tumor, [`mouse_tumor/seg/01-scan-3.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/tel-aviv/mouse_tumor/seg/01-scan-3.hdf5), reconstructed from the raw channel data with `mouse_tumor/pipeline.yaml`.*
+*Cine loop of the rotational sweep through a mouse tumor, [`mouse_tumor/seg/01-scan-3.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/tel-aviv/mouse_tumor/seg/01-scan-3.hdf5), reconstructed from the raw channel data with `mouse_tumor/pipeline.yaml`.*
## Dataset Description
@@ -45,7 +45,7 @@ Segmentation of tumors in mice and water-bead phantoms.
## Processing the Dataset
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/tel-aviv/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline.yaml` definitions in `mouse_tumor/` and `phantom/` and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub and overlays the stored segmentation on the reconstructed B-mode.
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/tel-aviv/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline.yaml` definitions in `mouse_tumor/` and `phantom/` and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub and overlays the stored segmentation on the reconstructed B-mode.
## Dataset Format
@@ -53,7 +53,7 @@ The acquisitions can be processed with the `reconstruct.py` [script](https://git
## Dataset Quantification
-**Current Open-H-RF release:** 22 HDF5 files; 4.36 GB (4,357,947,392 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 22 HDF5 files; 4.36 GB (4,357,947,392 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
- **Samples / frames:** 22 total `.hdf5` acquisitions. Each acquisition contains 144 angular frames covering a 180° rotation (1.25° increments) of 5 plane waves.
- **Train / val / test split:** N/A
diff --git a/tue-aaa/README.md b/tue-aaa/README.md
index 806d8a60e682088ce4cf5fefd7321f5b90b2ed42..221261198f671d6edda74224c06c498e46b33604 100644
--- a/tue-aaa/README.md
+++ b/tue-aaa/README.md
@@ -7,7 +7,7 @@ task_categories:
tags:
- ultrasound
- rf
- - open-h-rf
+ - openh-rf
- beamforming
- motion-estimation
- abdominal-aortic-aneurysm
@@ -20,7 +20,7 @@ language:
-*Cine loop of [`data/AAA_subject11.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/tue-aaa/data/AAA_subject11.hdf5), reconstructed from the raw channel data.*
+*Cine loop of [`data/AAA_subject11.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/tue-aaa/data/AAA_subject11.hdf5), reconstructed from the raw channel data.*
## Dataset Description
@@ -58,11 +58,11 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
```bash
zea process \
- --dataset hf://nvidia/Open-H-RF/tue-aaa/data/AAA_subject11.hdf5 \
- --config hf://nvidia/Open-H-RF/tue-aaa/pipeline.yaml
+ --dataset hf://nvidia/OpenH-RF/tue-aaa/data/AAA_subject11.hdf5 \
+ --config hf://nvidia/OpenH-RF/tue-aaa/pipeline.yaml
```
-Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/tue-aaa/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF).
+Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/tue-aaa/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF).
## Dataset Format
@@ -72,7 +72,7 @@ zea file format. Subject metadata is stored under `metadata/subject` (`age`, `se
## Dataset Quantification
-**Current Open-H-RF release:** 15 HDF5 files; 53.08 GB (53,077,606,400 bytes) stored; root `zea_version` **0.1.4**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 15 HDF5 files; 53.08 GB (53,077,606,400 bytes) stored; root `zea_version` **0.1.4**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
- **Samples / frames:** 15 acquisitions (one HDF5 file each), 500 frames per acquisition — 7,500 frames total
- **Stored HDF5 size:** 53.08 GB (53,077,606,400 bytes).
diff --git a/tue-cardiac/README.md b/tue-cardiac/README.md
index 98aec6df7937b1ae38bfe27ea9620f5e15251a9e..a720882bb2a67da1927612a5b92006ed0d1d1531 100644
--- a/tue-cardiac/README.md
+++ b/tue-cardiac/README.md
@@ -7,7 +7,7 @@ task_categories:
tags:
- ultrasound
- rf
- - open-h-rf
+ - openh-rf
- echocardiography
- cardiac
- plax
@@ -33,7 +33,7 @@ size_categories:
-*Parasternal long-axis cine loops of four volunteers, pulse-inversion harmonic tracks: focused ([`subject-002`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/tue-cardiac/data/subject-002.hdf5)), wide ([`subject-004`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/tue-cardiac/data/subject-004.hdf5)), focused ([`subject-006`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/tue-cardiac/data/subject-006.hdf5)) and wide ([`subject-009`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/tue-cardiac/data/subject-009.hdf5)).*
+*Parasternal long-axis cine loops of four volunteers, pulse-inversion harmonic tracks: focused ([`subject-002`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/tue-cardiac/data/subject-002.hdf5)), wide ([`subject-004`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/tue-cardiac/data/subject-004.hdf5)), focused ([`subject-006`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/tue-cardiac/data/subject-006.hdf5)) and wide ([`subject-009`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/tue-cardiac/data/subject-009.hdf5)).*
## Dataset Description
@@ -95,7 +95,7 @@ The dataset must not be used as a clinically validated diagnostic product.
## Processing the Dataset
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/tue-cardiac/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipelines/*.yaml` definitions in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/tue-cardiac/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipelines/*.yaml` definitions in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
Each file holds the eight transmit-encoding tracks. Set `ZEA_FILE`, `TRACK` and `FRAME` at the top of the script, together with the matching pipeline under `pipelines/` as `CONFIG` (`PIPELINE_FOR_TRACK` lists which track uses which; see also [Data Validation](#data-validation)). The image is written to `assets/`.
@@ -178,7 +178,7 @@ The source tensor order is preserved as `(n_frames, n_tx, n_ax, n_el, n_ch) = (1
Suggested citation for the dataset:
-> Penninga, S. W., & van Sloun, R. J. G. (2026). *TU/e Cardiac RF Multi-Transmit* [Data set]. Eindhoven University of Technology, Open-H-RF.
+> Penninga, S. W., & van Sloun, R. J. G. (2026). *TU/e Cardiac RF Multi-Transmit* [Data set]. Eindhoven University of Technology, OpenH-RF.
```bibtex
@misc{penninga_tue_cardiac_plax_2026,
@@ -186,13 +186,13 @@ Suggested citation for the dataset:
author = {Penninga, Simon W. and van Sloun, Ruud J. G.},
year = {2026},
publisher = {Biomedical Diagnostics Lab, Eindhoven University of Technology},
- howpublished = {Open-H-RF dataset},
+ howpublished = {OpenH-RF dataset},
}
```
## Dataset Quantification
-**Current Open-H-RF release:** 12 HDF5 files; 199.18 GB (199,180,025,844 bytes) stored; root `zea_version` **0.1.4**.
+**Current OpenH-RF release:** 12 HDF5 files; 199.18 GB (199,180,025,844 bytes) stored; root `zea_version` **0.1.4**.
- **Participants/files:** 12 distinct people represented by 12 HDF5 files.
- **Tracks/acquisitions:** 8 tracks per file; 96 acquisitions total.
- **Frames:** 100 per track; 9,600 frames total.
diff --git a/tue-carotid/README.md b/tue-carotid/README.md
index 5ef0862b2fb3954768fdb70cac7bafcd3f1d8b9f..4149ca121d424ef165e610166b5c777217e90050 100644
--- a/tue-carotid/README.md
+++ b/tue-carotid/README.md
@@ -8,7 +8,7 @@ task_categories:
tags:
- ultrasound
- rf
- - open-h-rf
+ - openh-rf
- carotid
- in-vivo
language:
@@ -20,7 +20,7 @@ language:
-*One cardiac cycle of a longitudinal bifurcation scan, [`data/5_long_bifur_R_0000.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/tue-carotid/data/5_long_bifur_R_0000.hdf5).*
+*One cardiac cycle of a longitudinal bifurcation scan, [`data/5_long_bifur_R_0000.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/tue-carotid/data/5_long_bifur_R_0000.hdf5).*
## Dataset Description
@@ -76,11 +76,11 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
```bash
zea process \
- --dataset hf://nvidia/Open-H-RF/tue-carotid/data/5_long_bifur_R_0000.hdf5 \
- --config hf://nvidia/Open-H-RF/tue-carotid/pipeline.yaml
+ --dataset hf://nvidia/OpenH-RF/tue-carotid/data/5_long_bifur_R_0000.hdf5 \
+ --config hf://nvidia/OpenH-RF/tue-carotid/pipeline.yaml
```
-Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/tue-carotid/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF).
+Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/tue-carotid/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF).
## Dataset Format
@@ -88,7 +88,7 @@ Alternatively, you can use the `reconstruct.py` [script](https://github.com/open
## Dataset Quantification
-**Open-H-RF release:** 80 HDF5 files; 435.40 GB (435,403,030,528 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**OpenH-RF release:** 80 HDF5 files; 435.40 GB (435,403,030,528 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
- **Samples / frames / acquisitions:** 80 acquisitions (one HDF5 file each) from 10 subjects, 150 frames per acquisition — 12,000 frames total.
- **Transmit events per frame:** 149 — 128 focused lines interleaved with 21 plane waves.
diff --git a/tumunich/README.md b/tumunich/README.md
index 5acc4cfc1a8cc480a2df204fbb271244714d1cd9..08632aa3186d7c786a970a62c41afad62dfa07a3 100644
--- a/tumunich/README.md
+++ b/tumunich/README.md
@@ -7,7 +7,7 @@ task_categories:
tags:
- ultrasound
- rf
- - open-h-rf
+ - openh-rf
- 3d
- robotic
- freehand
@@ -31,7 +31,7 @@ size_categories:
-*Acquisition setup (left), a single-frame reconstruction (middle) and a panoramic reconstruction compounded along the robot-tracked sweep (right) of the CIRS 054GS phantom, [`data/cirs_phantom/synth_apert_sweep_1.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/tumunich/data/cirs_phantom/synth_apert_sweep_1.hdf5).*
+*Acquisition setup (left), a single-frame reconstruction (middle) and a panoramic reconstruction compounded along the robot-tracked sweep (right) of the CIRS 054GS phantom, [`data/cirs_phantom/synth_apert_sweep_1.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/tumunich/data/cirs_phantom/synth_apert_sweep_1.hdf5).*
## Dataset Description
@@ -72,9 +72,9 @@ Primary task: **robotic tracked ultrasound acquisition** — compounding the per
## Processing the Dataset
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/tumunich/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/tumunich/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
-`reconstruct.py` beamforms a single frame and compares it to the stored Verasonics B-mode; [`reconstruct_panorama.py`](https://github.com/open-h/Open-H-RF/blob/main/datasets/tumunich/reconstruct_panorama.py), with `pipeline_panorama.yaml`, compounds a tracked sweep into the panoramic reconstruction shown above, using the per-frame robot poses.
+`reconstruct.py` beamforms a single frame and compares it to the stored Verasonics B-mode; [`reconstruct_panorama.py`](https://github.com/open-h/OpenH-RF/blob/main/datasets/tumunich/reconstruct_panorama.py), with `pipeline_panorama.yaml`, compounds a tracked sweep into the panoramic reconstruction shown above, using the per-frame robot poses.
## Dataset Format
@@ -88,7 +88,7 @@ Zero-valued rows pad the top of each stored frame so the image starts at the tra
## Dataset Quantification
-**Current Open-H-RF release:** 11 HDF5 files; 62.25 GB (62,245,175,296 bytes) stored; root `zea_version` **0.1.4**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 11 HDF5 files; 62.25 GB (62,245,175,296 bytes) stored; root `zea_version` **0.1.4**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
- **Phantoms:** 3 (vascular access arm phantom, CIRS 054GS, CIRS 074 thyroid)
- **Files:** 11 zea HDF5 files across 3 sub-dataset folders
diff --git a/twente-cavitation/README.md b/twente-cavitation/README.md
index aaec9c5b679da505e967f7a6b941ec9767f923dc..d9a3ecc6790b5a9d4ce2e735235cb7e2bc716791 100644
--- a/twente-cavitation/README.md
+++ b/twente-cavitation/README.md
@@ -6,27 +6,19 @@ task_categories:
- image-classification
tags:
- ultrasound
- - rf
- - open-h-rf
+ - rf
+ - openh-rf
- cavitation
language:
- en
size_categories:
- - 1K.png`.
@@ -59,11 +51,11 @@ Set `ZEA_FILE` at the top of the script to pick an acquisition and `N_FRAMES` to
[zea v0.1.6](https://github.com/tue-bmd/zea)
-[zea file format](https://zea.readthedocs.io/en/latest/data-acquisition.html). No preprocessing is applied.
+.zea file format. No preprocessing is applied.
## Dataset Quantification
-**Current Open-H-RF release:** 19 HDF5 files; 10.85 GB (10,850,533,376 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 19 HDF5 files; 10.85 GB (10,850,533,376 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
- 19 aquisitions of 400 frames each, totalling 7600 frames
- No train/validation/test split is defined; all acquisitions are provided in full.
@@ -119,11 +111,8 @@ Files are named `cavitation_bubbles_kPa_mL.hdf5`, where `.png`. Usage:
-```bash
-python reconstruct.py
-```
+`reconstruct.py` reconstructs a passive acoustic map (PAM) with the `zea.Pipeline` in `pipeline.yaml`: the array only receives, so the transmit model is overridden and the chain aligns purely on receive curvature (one-way passive beamforming), averaging the envelope energy over sampling instants and frames. An example output is [`assets/cavitation_bubbles_10kPa_01mL_per_min.png`](assets/cavitation_bubbles_10kPa_01mL_per_min.png).
## Known Issues
No known issues.
diff --git a/twente-cavitation/assets/Schematic_setup.png b/twente-cavitation/assets/Schematic_setup.png
deleted file mode 100644
index c8949b82067ae2a0439ad7c0ba3c425ce71435a9..0000000000000000000000000000000000000000
--- a/twente-cavitation/assets/Schematic_setup.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e8376f0037c5f258fb055da8b144993faec0bcb96d7048d85c6e66d375b1c087
-size 24628
diff --git a/twente-cavitation/assets/cavitation_bubbles_1000kPa_01mL_per_min.png b/twente-cavitation/assets/cavitation_bubbles_1000kPa_01mL_per_min.png
deleted file mode 100644
index 3135fb3f9299d27ca4d7b080f3b2fe391198dbc4..0000000000000000000000000000000000000000
--- a/twente-cavitation/assets/cavitation_bubbles_1000kPa_01mL_per_min.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:edd71ac77994ce0c631c33b45895baa2cc7710458921781a5c1a4bfeec75cd1b
-size 90392
diff --git a/twente-cavitation/assets/cavitation_bubbles_10kPa_01mL_per_min.png b/twente-cavitation/assets/cavitation_bubbles_10kPa_01mL_per_min.png
new file mode 100644
index 0000000000000000000000000000000000000000..46c326031c7a39fc519baee945e9231be274cea5
--- /dev/null
+++ b/twente-cavitation/assets/cavitation_bubbles_10kPa_01mL_per_min.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0149458b4753f21112af8f259a3d2d7bf8dd2a90007eedeccfb3210e1cf79896
+size 357497
diff --git a/twente-cavitation/assets/hero.png b/twente-cavitation/assets/hero.png
deleted file mode 100644
index cb08dcab5e6f47978ad66bcc2e8ccdfd1b4ea7d9..0000000000000000000000000000000000000000
--- a/twente-cavitation/assets/hero.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:cf8d08295252e327a5e71e66227bc629f4c38fa44864646dc4f83ecfe38f844d
-size 42321
diff --git a/twente-cavitation/pipeline.yaml b/twente-cavitation/pipeline.yaml
index 03f9175e8da59a6207bc211a75e67cf13e500c62..3cab04f7ffde501c63f1cabcc9bd52fa6fab6ed4 100644
--- a/twente-cavitation/pipeline.yaml
+++ b/twente-cavitation/pipeline.yaml
@@ -11,15 +11,12 @@ pipeline:
diagonal_loading: 0.01
- envelope_detect
parameters:
- grid_size_x: 200
- grid_size_z: 300
+ grid_size_x: 387
+ grid_size_z: 577
xlims:
- - -0.01
- - 0.01
+ - -0.019
+ - 0.019
zlims:
- - 0.01
- - 0.04
+ - 0.002
+ - 0.06
apply_lens_correction: true
- dynamic_range:
- - -20
- - 0
diff --git a/twente-microbubblesim/README.md b/twente-microbubblesim/README.md
index 4a9df69e192eeaf769346e1eae9869dff68c19e7..899fa4eed61423b32e9be4db09c509264908bdba 100644
--- a/twente-microbubblesim/README.md
+++ b/twente-microbubblesim/README.md
@@ -9,7 +9,7 @@ tags:
- rf
- microbubbles
- synthetic-data
- - open-h-rf
+ - openh-rf
language:
- en
size_categories:
@@ -56,7 +56,7 @@ The dataset is intended for studying the effect of transmit waveforms on deep-le
## Processing the Dataset
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/twente-microbubblesim/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the pipeline definitions in `pipeline/` and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/twente-microbubblesim/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the pipeline definitions in `pipeline/` and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
Every pulse track has its own verified pipeline file, `pipeline/pipeline_track__
Open-H-RF
+
- 
- 
- 
-
-*3D power Doppler of the transcranial CEUS acquisition [`clinical/SP03-Left/SP03-Left.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/resolvestroke/clinical/SP03-Left/SP03-Left.hdf5) at bolus +10 s, x-z maximum-intensity projection, reconstructed from the released channel data.*
+*3D power Doppler of the transcranial CEUS acquisition [`clinical/SP03-Left/SP03-Left.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/resolvestroke/clinical/SP03-Left/SP03-Left.hdf5) at bolus +10 s, x-z maximum-intensity projection, reconstructed from the released channel data.*
## Dataset Description
-[Resolve Stroke](https://www.resolvestroke.com/) develops SYLVER, a software-driven ultrasound platform that supports clinical assessment in patients suspected of, or at risk for, cerebral perfusion abnormalities by improving visualization of cerebral vasculature and providing complementary information on cerebral perfusion. SYLVER images with a 32×32 matrix array probe at 2 MHz using diverging-wave transmits. CE and FDA clearances are expected in 2026. This directory holds the data Resolve Stroke contributes to Open-H-RF: pre-beamformed RF/IQ channel data from an imaging phantom, a flow phantom, and in-vivo transcranial acquisitions, all de-identified and released under CC BY 4.0. The in-vivo data comes from the CPP-approved SCULPT clinical study. Two transmit sequences are included: a saddle sequence that images a 2D plane for real-time B-mode, and a 4 kHz single-aperture volume sequence for CEUS and blood-flow measurement.
+[Resolve Stroke](https://www.resolvestroke.com/) develops SYLVER, a software-driven ultrasound platform that supports clinical assessment in patients suspected of, or at risk for, cerebral perfusion abnormalities by improving visualization of cerebral vasculature and providing complementary information on cerebral perfusion. SYLVER images with a 32×32 matrix array probe at 2 MHz using diverging-wave transmits. CE and FDA clearances are expected in 2026. This directory holds the data Resolve Stroke contributes to OpenH-RF: pre-beamformed RF/IQ channel data from an imaging phantom, a flow phantom, and in-vivo transcranial acquisitions, all de-identified and released under CC BY 4.0. The in-vivo data comes from the CPP-approved SCULPT clinical study. Two transmit sequences are included: a saddle sequence that images a 2D plane for real-time B-mode, and a 4 kHz single-aperture volume sequence for CEUS and blood-flow measurement.
Each dataset directory holds its data card (`README.md`), a `reconstruct.py` + `pipeline.yaml` beamforming recipe and, for the CEUS datasets, a power-Doppler reconstruction; the HDF5 files are on the Hub and the preview images in [`assets/`](assets/).
@@ -53,7 +53,7 @@ Each dataset directory holds its data card (`README.md`), a `reconstruct.py` + `
## Processing the Dataset
-Each sub-dataset has its own `reconstruct.py` and `pipeline.yaml`, as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF/tree/main/datasets/resolvestroke), built on the [zea library](https://github.com/tue-bmd/zea); the CEUS datasets add a power-Doppler reconstruction. See the data card of each sub-dataset for how to run them.
+Each sub-dataset has its own `reconstruct.py` and `pipeline.yaml`, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF/tree/main/datasets/resolvestroke), built on the [zea library](https://github.com/tue-bmd/zea); the CEUS datasets add a power-Doppler reconstruction. See the data card of each sub-dataset for how to run them.
The Python scripts carry their own `SPDX-License-Identifier: Apache-2.0` header; the dataset itself is CC BY 4.0.
@@ -63,7 +63,7 @@ The Python scripts carry their own `SPDX-License-Identifier: Apache-2.0` header;
## Dataset Quantification
-**Current Open-H-RF release:** 43 HDF5 files; 110.62 GB (110,619,394,048 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
+**Current OpenH-RF release:** 43 HDF5 files; 110.62 GB (110,619,394,048 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
## Contents
diff --git a/resolvestroke/clinical/README.md b/resolvestroke/clinical/README.md
index 2690be3e43335c07a099ae0d439557f1fbf7d3c3..0b24417ffa4dbf4963c43e5708bb471beb0b3c90 100644
--- a/resolvestroke/clinical/README.md
+++ b/resolvestroke/clinical/README.md
@@ -7,7 +7,7 @@ task_categories:
tags:
- ultrasound
- iq
- - open-h-rf
+ - openh-rf
- 3d
- matrix-probe
- contrast-enhanced
@@ -24,7 +24,7 @@ size_categories:
-*Reference microvascular image (`mvi`, x-z maximum-intensity projection of the computed reference) for the 20 acquisitions in [`clinical/`](https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical).*
+*Reference microvascular image (`mvi`, x-z maximum-intensity projection of the computed reference) for the 20 acquisitions in [`clinical/`](https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical).*
## Dataset Description
@@ -71,12 +71,12 @@ Contrast-enhanced ultrasound flow/perfusion quantification, microbubble detectio
## Processing the Dataset
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/resolvestroke/clinical/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/resolvestroke/clinical/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
-The reconstruction recipe is identical for all 20 acquisitions, so the scripts and pipeline YAMLs live once in this directory. Each script has a `SUBJECT` constant at the top (default `"SP02-Left-2"`); set it to any acquisition name from the inventory, e.g. `SUBJECT = "SP07-Right"`, and the `hf://` input path `hf://nvidia/Open-H-RF/resolvestroke/clinical/
-*B-mode of a fractured-femur phantom, frame 45 of [`data/phantom2_wholebone.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/strasbourg-basel/BoneSRF/data/phantom2_wholebone.hdf5), beamformed from the recovered channel data.*
+*B-mode of a fractured-femur phantom, frame 45 of [`data/phantom2_wholebone.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/strasbourg-basel/BoneSRF/data/phantom2_wholebone.hdf5), beamformed from the recovered channel data.*
## Dataset Description
@@ -37,7 +37,7 @@ size_categories:
-**BoneSRF** (Bone Surface Reflection) is an ultrasound channel-data dataset for bone-surface and fracture-reflection research. It contains three 3D-printed, fractured femur phantoms, scanned with a handheld point-of-care probe, inverted back to pre-beamformed RF and packaged in the Open-H-RF `zea` format.
+**BoneSRF** (Bone Surface Reflection) is an ultrasound channel-data dataset for bone-surface and fracture-reflection research. It contains three 3D-printed, fractured femur phantoms, scanned with a handheld point-of-care probe, inverted back to pre-beamformed RF and packaged in the OpenH-RF `zea` format.
Each phantom was swept three times (`distal`, `proximal`, `wholebone`), giving nine `zea` HDF5 files. Every scan is robot-tracked: the probe was mounted on a robotic arm and its pose recorded separately. Each file also carries that phantom's CT scan and multi-label segmentation.
@@ -49,7 +49,7 @@ This is phantom data, not simulated, clinical or in-vivo. It is intended for ful
1. **Acquisition.** Each phantom was scanned with a Clarius L20HD3 linear array, producing the probe's own beamformed RF output. This is the physically acquired data, not simulated.
2. **Inversion.** The beamformed RF is inverted back into pre-beamformed, per-element channel data with [`das-inverse` (`clarius` branch)](https://github.com/sankethvedula/das-inverse/tree/clarius), a CGLS solver over a `zea.inverse.DASOperator` forward model of the probe's focused, walking-sub-aperture transmit sequence (`invert_clarius_beamformed.py`). The result is not sensor-captured, but numerically consistent with the real beamformed acquisition it was inverted from.
-3. **Packaging.** The inverted channel data, transmit-sequence metadata, probe geometry, per-frame probe pose, and the phantom's CT + segmentation are written out as one `zea` HDF5 file per scan, matching the Open-H-RF format spec.
+3. **Packaging.** The inverted channel data, transmit-sequence metadata, probe geometry, per-frame probe pose, and the phantom's CT + segmentation are written out as one `zea` HDF5 file per scan, matching the OpenH-RF format spec.
### Probe tracking
@@ -91,7 +91,7 @@ The CT and segmentation data inside the files are released under the same CC BY
## Processing the Dataset
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/strasbourg-basel/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/strasbourg-basel/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
The script loads the saved `zea.Pipeline` from `pipeline.yaml`, runs it on one frame of one scan, and writes `assets/