Tidy the pipeline YAMLs

.gitattributes

@@ -1,3 +1,5 @@
+* text=auto eol=lf
+
 *.7z filter=lfs diff=lfs merge=lfs -text
 *.arrow filter=lfs diff=lfs merge=lfs -text
 *.avro filter=lfs diff=lfs merge=lfs -text

README.md

@@ -12,6 +12,8 @@ This dataset is a collection of RF samples and metadata in the [`zea` file forma
 
 This dataset is ready for commercial or non-commercial uses.
 
+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).
+
 ## Dataset Owner
 
 NVIDIA Corporation

colorado-boulder/README.md

@@ -1,174 +1,174 @@
----
-pretty_name: "OpenH-RF — Tracked Swept Synthetic Aperture 3D Phantom Dataset"
-license: cc-by-4.0
-task_categories:
-  - generalized-reconstruction
-tags:
-  - ultrasound
-  - rf
-  - openh-rf
-  - 3d
-language:
-  - en
-size_categories:
-  - n<1K
----
-
-# Tracked Swept Synthetic Aperture 3D Phantom Ultrasound Dataset
-
-## Dataset Description
-
-This dataset contains tracked swept synthetic aperture (SSA) ultrasound acquisitions of a 3D ultrasound imaging phantom. 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. Its purpose is to provide a reproducible example of motion-compensated SSA reconstruction from raw channel data using the zea/OpenH-RF data format.
-
-This dataset contains phantom data only. It does not contain human subject data, animal data, or protected health information (PHI).
-
-## Dataset Contributor(s)
-
-**Contributing organization:** University of Colorado Boulder, Bottenus Lab
-
-**Contributors:**
-
-- Anet Sanchez
-- Nick Bottenus
-
-## Dataset Creation Date
-
-06/24/2025
-
-## License / Terms of Use
-
-This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
-
-The contributed data consist exclusively of phantom ultrasound acquisitions and are cleared for release under CC BY 4.0. Patient consent, clinical data-use agreements, and PHI de-identification are not applicable because the dataset does not contain human subject data.
-
-## Intended Usage
-
-This dataset is intended for research on generalized ultrasound reconstruction, with a particular focus on tracked swept synthetic aperture imaging, motion-compensated beamforming, coherent compounding, and ultrasound image-quality evaluation.
-
-For SSA reconstruction, each raw RF frame is beamformed using its corresponding tracked transducer pose. The resulting beamformed IQ frames are placed on a common reconstruction grid and coherently summed to synthesize a larger effective aperture. Because the reconstruction relies on coherent compounding, summation is performed before envelope detection, normalization, and log compression.
-
-## Dataset Characterization
-
-- **Data Collection Method:** Phantom ultrasound acquisition
-- **Labeling Method:** N/A; no manual labels or segmentation masks are provided
-- **Acquisition System:** Verasonics Vantage research ultrasound scanner with a Verasonics P4-2 phased array transducer
-
-### Acquisition Details
-
-The transducer was manually swept over the phantom field of view while diverging-wave transmissions were acquired at 400 Hz. All 64 array elements were used on receive.
-
-Diverging waves were generated using a negative virtual source with the 20 central array elements active on transmit.
-
-The transducer was optically tracked using an NDI Polaris Vega® XT optical tracking system manufactured by Northern Digital Inc., Ontario, Canada.
-
-### Probe and Geometry
-
-The acquisition used a Verasonics P4-2 phased array transducer with 64 elements. The center frequency stored in the acquisition and used for reconstruction is 2.5 MHz.
-
-The probe geometry, transmit origins, transmit delays, transmit apodization, and other acquisition parameters are stored in the zea/OpenH-RF file. Frame-wise probe translations and rotations are stored using the native `metadata/probe_pose` structure.
-
-## Dataset Format
-
-The dataset is distributed in the zea/OpenH-RF HDF5 format.
-
-Each file includes:
-
-- Raw RF channel data
-- Sampling and center frequencies
-- Transmit delays and apodization
-- Transmit origins
-- Probe geometry
-- Sound-speed information
-- Frame-wise tracked probe translations
-- Frame-wise tracked probe rotations
-
-The stored RF channel data have not been beamformed, envelope detected, normalized, or log compressed. The accompanying reconstruction pipeline performs these processing steps.
-
-## Dataset Quantification
-
-**Current OpenH-RF release:** 62 HDF5 files; 16.04 GB (16,037,117,952 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 phantom objects:** 1
-- **Number of acquisitions:** 10
-- **Number of RF frames per acquisition:** 1200
-- **Number of transmit events per frame:** 1
-- **Number of receive elements:** 64
-- **Number of active transmit elements:** 20
-
-### Per-File Feature Summary
-
-| Feature | Shape | Data type | Units | Description |
-|---|---:|---|---|---|
-| Raw RF data | `n_frames × n_tx × n_ax × n_elements × n_channels` | `float32` | acquisition units | Raw RF channel measurements |
-| Probe translation | `n_frames × 3` | `float32` | m | Frame-wise tracked probe position |
-| Probe rotation | `n_frames × 4` | `float32` | unit quaternion | Frame-wise tracked probe orientation in `xyzw` order |
-| Probe geometry | `64 × 3` | `float32` | m | Array-element coordinates |
-| Transmit origins | `n_tx × 3` | `float32` | m | Diverging-wave virtual-source coordinates |
-| Transmit delays | `n_tx × 64` | `float32` | s | Per-element transmit delays |
-| Transmit apodization | `n_tx × 64` | `float32` | unitless | Per-element transmit activation and weighting |
-
-## Subject Metadata
-
-### Metadata Schema Migration
-
-The zea 0.1.6 migration uses these approved metadata locations:
-
-| Legacy location | Canonical location |
-|---|---|
-| Dataset `metadata/subject_id` | Dataset `metadata/subject/id` |
-| Dataset `metadata/subject_type` | Dataset `metadata/subject/type` |
-| Dataset `metadata/us_machine` | Root HDF5 attribute `us_machine` |
-
-Read the machine name with `f.attrs["us_machine"]`, not `f["us_machine"]`.
-Subject values and their existing attributes are preserved. The machine
-string is preserved; migration stops for review if its legacy dataset has
-attributes that cannot be represented without loss. No numerical arrays are
-rescaled or otherwise changed by these relocations.
-
-The three-field pilot passed full array and metadata parity checks with the
-approved description changes and `transmit_only=False` default. Full-release
-migration is still pending. Replacement files are uploaded only after
-per-file validation; readers supporting both revisions should check the
-canonical locations first, then the legacy locations.
-
-
-This dataset contains one 3D ultrasound imaging phantom.
-
-- **Subject type:** 3D phantom
-- **Anatomical region:** Not applicable
-- **Human participants:** None
-- **Animal subjects:** None
-- **Protected health information:** None
-- **Scanner:** Verasonics Vantage
-- **Probe:** Verasonics P4-2 phased array
-
-## Data Validation
-
-The submission includes a `zea.Pipeline` that reconstructs a representative tracked SSA B-mode image from the raw RF channel data.
-
-The pipeline performs:
-
-1. Frame-wise demodulation
-2. Application of the tracked probe pose
-3. Delay-and-sum beamforming onto a common reconstruction grid
-4. Coherent summation of the beamformed IQ frames
-5. Envelope detection
-6. Normalization
-7. Log compression
-
-The reconstruction is defined in `pipeline.yaml` and executed using `reconstruct.py`. A representative reconstructed B-mode image is included with the dataset.
-
-## Known Issues
-
-- Optical tracking measurements may contain small position and orientation uncertainties.
-- Reconstruction quality depends on tracking calibration accuracy and coherent alignment between frames.
-
-
-## Ethical Considerations
-
-This dataset contains phantom ultrasound data only. It does not contain human participants, animal subjects, personal identifiers, clinical records, or protected health information.
-
-Human-subject consent and institutional review board approval are therefore not applicable.
-
+---
+pretty_name: "OpenH-RF — Tracked Swept Synthetic Aperture 3D Phantom Dataset"
+license: cc-by-4.0
+task_categories:
+  - generalized-reconstruction
+tags:
+  - ultrasound
+  - rf
+  - openh-rf
+  - 3d
+language:
+  - en
+size_categories:
+  - n<1K
+---
+
+# Tracked Swept Synthetic Aperture 3D Phantom Ultrasound Dataset
+
+## Dataset Description
+
+This dataset contains tracked swept synthetic aperture (SSA) ultrasound acquisitions of a 3D ultrasound imaging phantom. 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. Its purpose is to provide a reproducible example of motion-compensated SSA reconstruction from raw channel data using the zea/OpenH-RF data format.
+
+This dataset contains phantom data only. It does not contain human subject data, animal data, or protected health information (PHI).
+
+## Dataset Contributor(s)
+
+**Contributing organization:** University of Colorado Boulder, Bottenus Lab
+
+**Contributors:**
+
+- Anet Sanchez
+- Nick Bottenus
+
+## Dataset Creation Date
+
+06/24/2025
+
+## License / Terms of Use
+
+This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
+
+The contributed data consist exclusively of phantom ultrasound acquisitions and are cleared for release under CC BY 4.0. Patient consent, clinical data-use agreements, and PHI de-identification are not applicable because the dataset does not contain human subject data.
+
+## Intended Usage
+
+This dataset is intended for research on generalized ultrasound reconstruction, with a particular focus on tracked swept synthetic aperture imaging, motion-compensated beamforming, coherent compounding, and ultrasound image-quality evaluation.
+
+For SSA reconstruction, each raw RF frame is beamformed using its corresponding tracked transducer pose. The resulting beamformed IQ frames are placed on a common reconstruction grid and coherently summed to synthesize a larger effective aperture. Because the reconstruction relies on coherent compounding, summation is performed before envelope detection, normalization, and log compression.
+
+## Dataset Characterization
+
+- **Data Collection Method:** Phantom ultrasound acquisition
+- **Labeling Method:** N/A; no manual labels or segmentation masks are provided
+- **Acquisition System:** Verasonics Vantage research ultrasound scanner with a Verasonics P4-2 phased array transducer
+
+### Acquisition Details
+
+The transducer was manually swept over the phantom field of view while diverging-wave transmissions were acquired at 400 Hz. All 64 array elements were used on receive.
+
+Diverging waves were generated using a negative virtual source with the 20 central array elements active on transmit.
+
+The transducer was optically tracked using an NDI Polaris Vega® XT optical tracking system manufactured by Northern Digital Inc., Ontario, Canada.
+
+### Probe and Geometry
+
+The acquisition used a Verasonics P4-2 phased array transducer with 64 elements. The center frequency stored in the acquisition and used for reconstruction is 2.5 MHz.
+
+The probe geometry, transmit origins, transmit delays, transmit apodization, and other acquisition parameters are stored in the zea/OpenH-RF file. Frame-wise probe translations and rotations are stored using the native `metadata/probe_pose` structure.
+
+## Dataset Format
+
+The dataset is distributed in the zea/OpenH-RF HDF5 format.
+
+Each file includes:
+
+- Raw RF channel data
+- Sampling and center frequencies
+- Transmit delays and apodization
+- Transmit origins
+- Probe geometry
+- Sound-speed information
+- Frame-wise tracked probe translations
+- Frame-wise tracked probe rotations
+
+The stored RF channel data have not been beamformed, envelope detected, normalized, or log compressed. The accompanying reconstruction pipeline performs these processing steps.
+
+## Dataset Quantification
+
+**Current OpenH-RF release:** 62 HDF5 files; 16.04 GB (16,037,117,952 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 phantom objects:** 1
+- **Number of acquisitions:** 10
+- **Number of RF frames per acquisition:** 1200
+- **Number of transmit events per frame:** 1
+- **Number of receive elements:** 64
+- **Number of active transmit elements:** 20
+
+### Per-File Feature Summary
+
+| Feature | Shape | Data type | Units | Description |
+|---|---:|---|---|---|
+| Raw RF data | `n_frames × n_tx × n_ax × n_elements × n_channels` | `float32` | acquisition units | Raw RF channel measurements |
+| Probe translation | `n_frames × 3` | `float32` | m | Frame-wise tracked probe position |
+| Probe rotation | `n_frames × 4` | `float32` | unit quaternion | Frame-wise tracked probe orientation in `xyzw` order |
+| Probe geometry | `64 × 3` | `float32` | m | Array-element coordinates |
+| Transmit origins | `n_tx × 3` | `float32` | m | Diverging-wave virtual-source coordinates |
+| Transmit delays | `n_tx × 64` | `float32` | s | Per-element transmit delays |
+| Transmit apodization | `n_tx × 64` | `float32` | unitless | Per-element transmit activation and weighting |
+
+## Subject Metadata
+
+### Metadata Schema Migration
+
+The zea 0.1.6 migration uses these approved metadata locations:
+
+| Legacy location | Canonical location |
+|---|---|
+| Dataset `metadata/subject_id` | Dataset `metadata/subject/id` |
+| Dataset `metadata/subject_type` | Dataset `metadata/subject/type` |
+| Dataset `metadata/us_machine` | Root HDF5 attribute `us_machine` |
+
+Read the machine name with `f.attrs["us_machine"]`, not `f["us_machine"]`.
+Subject values and their existing attributes are preserved. The machine
+string is preserved; migration stops for review if its legacy dataset has
+attributes that cannot be represented without loss. No numerical arrays are
+rescaled or otherwise changed by these relocations.
+
+The three-field pilot passed full array and metadata parity checks with the
+approved description changes and `transmit_only=False` default. Full-release
+migration is still pending. Replacement files are uploaded only after
+per-file validation; readers supporting both revisions should check the
+canonical locations first, then the legacy locations.
+
+
+This dataset contains one 3D ultrasound imaging phantom.
+
+- **Subject type:** 3D phantom
+- **Anatomical region:** Not applicable
+- **Human participants:** None
+- **Animal subjects:** None
+- **Protected health information:** None
+- **Scanner:** Verasonics Vantage
+- **Probe:** Verasonics P4-2 phased array
+
+## Data Validation
+
+The submission includes a `zea.Pipeline` that reconstructs a representative tracked SSA B-mode image from the raw RF channel data.
+
+The pipeline performs:
+
+1. Frame-wise demodulation
+2. Application of the tracked probe pose
+3. Delay-and-sum beamforming onto a common reconstruction grid
+4. Coherent summation of the beamformed IQ frames
+5. Envelope detection
+6. Normalization
+7. Log compression
+
+The reconstruction is defined in `pipeline.yaml` and executed using `reconstruct.py`. A representative reconstructed B-mode image is included with the dataset.
+
+## Known Issues
+
+- Optical tracking measurements may contain small position and orientation uncertainties.
+- Reconstruction quality depends on tracking calibration accuracy and coherent alignment between frames.
+
+
+## Ethical Considerations
+
+This dataset contains phantom ultrasound data only. It does not contain human participants, animal subjects, personal identifiers, clinical records, or protected health information.
+
+Human-subject consent and institutional review board approval are therefore not applicable.
+

colorado-boulder/pipeline.yaml

@@ -1,45 +1,44 @@
-pipeline:
-    operations:
-    -   name: map
-        operations:
-        -   name: keras.ops.squeeze
-            params:
-                axis: 0
-                jit_compile: false
-        -   name: keras.ops.cast
-            params:
-                dtype: float32
-                jit_compile: false
-        -   name: demodulate
-            params:
-                jit_compile: false
-        -   name: apply_probe_pose
-            params:
-                jit_compile: false
-        -   name: beamform
-            params:
-                jit_options: null
-                num_patches: 200
-                with_batch_dim: false
-        -   name: keras.ops.expand_dims
-            params:
-                axis: 0
-                jit_compile: false
-        params:
-            argnames:
-            - data
-            - probe_translation
-            - probe_rotation
-            batch_size: 1
-            with_batch_dim: false
-    -   name: keras.ops.sum
-        params:
-            axis: 0
-    - envelope_detect
-    -   name: normalize
-        params:
-            output_range:
-            - 0.0
-            - 1.0
-    - log_compress
-    with_batch_dim: false
+pipeline:
+    operations:
+    -   name: map
+        operations:
+        -   name: keras.ops.squeeze
+            params:
+                axis: 0
+                jit_compile: false
+        -   name: keras.ops.cast
+            params:
+                dtype: float32
+                jit_compile: false
+        -   name: demodulate
+            params:
+                jit_compile: false
+        -   name: apply_probe_pose
+            params:
+                jit_compile: false
+        -   name: beamform
+            params:
+                jit_options: null
+                with_batch_dim: false
+        -   name: keras.ops.expand_dims
+            params:
+                axis: 0
+                jit_compile: false
+        params:
+            argnames:
+            - data
+            - probe_translation
+            - probe_rotation
+            batch_size: 1
+            with_batch_dim: false
+    -   name: keras.ops.sum
+        params:
+            axis: 0
+    - envelope_detect
+    -   name: normalize
+        params:
+            output_range:
+            - 0.0
+            - 1.0
+    - log_compress
+    with_batch_dim: false

concordia/README.md

@@ -137,7 +137,7 @@ Fields from Arbitrarily Shaped, Apodized, and Excited Ultrasound Transducers,"
 
 The dataset is 2,000 individual zea HDF5 files (one acquisition per file) under
 `data/` (zea format; `zea_version` 0.1.6). Reference figures are in
-`examples/`; `reconstruct.py`, `visualize.py`, `pipeline.yaml`, and this card sit
+`examples/`; `reconstruct.py`, `pipeline.yaml`, and this card sit
 at the repository root. Per file:
 
 - `data/raw_data` — full FSA channel data, `int16`, quantized from the native
@@ -244,25 +244,23 @@ scatterers (amplitude 18–22) scattered at valid random positions, for realism.
 
 ## Data Validation
 
-**Setup:** `reconstruct.py` and `visualize.py` require `zea>=0.1.1`, `matplotlib`,
+**Setup:** `reconstruct.py` requires `zea>=0.1.1`, `matplotlib`,
 and `KERAS_BACKEND=jax` (or `torch`/`tensorflow`) set before import. This submission
 was built and verified against the [OpenH-RF repo](https://github.com/open-h/OpenH-RF)'s environment (`uv sync` inside a clone of that repo installs `zea` and every other
-dependency these scripts need — see that repo's README for the exact commands).
+dependency this script needs — see that repo's README for the exact commands).
 
 `reconstruct.py` (+ `pipeline.yaml`, zea's default DAS pipeline: Cast → ApplyWindow →
 Demodulate → Beamform → EnvelopeDetect → Normalize → LogCompress; each stage is
 explained in `reconstruct.py`'s own module docstring) reconstructs a synthetic
 transmit aperture (STA) B-mode from `data/raw_data` using all 128 transmits, over
-the same field of view as the stored `data/image` reference. A second helper,
-`visualize.py <file>.hdf5`, renders the stored `data/image`, its class-specific
-label, and the `data/scatterers` cloud side by side. `examples/` contains, for a
-representative capture of each of the five classes, an `<id>_bmode.png` (the STA
-reference reconstruction from `reconstruct.py`, physical mm axes, titled with the
-sample ID) and an `<id>_panels.png` (the `visualize.py` output: stored `data/image`,
-class label — segmentation foreground for anechoic/hypoechoic/hyperechoic,
+the same field of view as the stored `data/image` reference. It renders that
+reconstruction on physical mm axes next to the capture's class-specific label
+— segmentation foreground for anechoic/hypoechoic/hyperechoic,
 `data/diverse_source_image` for diverse, none for point-target — and the
-`data/scatterers` cloud coloured by |amplitude|, on shared equal-aspect mm axes),
-confirming the label, reconstruction, and scatterer field are spatially registered.
+`data/scatterers` cloud coloured by |amplitude|, all on shared equal-aspect mm
+axes, confirming the label, reconstruction, and scatterer field are spatially
+registered. `examples/` holds one such figure for a representative capture of
+each of the five classes.
 
 ## Known Issues
 

concordia/pipeline.yaml

@@ -5,9 +5,7 @@ pipeline:
             dtype: float32
     - apply_window
     - demodulate
-    -   name: beamform
-        params:
-            num_patches: 200
+    - beamform
     - envelope_detect
     -   name: normalize
         params:

dartmouth-uct/pipeline.yaml

@@ -11,7 +11,6 @@ pipeline:
         params:
             with_batch_dim: false
             jit_options: null
-            num_patches: 64
     - reshape_grid
     -   name: normalize
         params:

kaist-snubh-barreleye/pipeline.yaml

@@ -10,9 +10,7 @@ pipeline:
             - 1000000.0
             - 12000000.0
     - demodulate
-    -   name: beamform
-        params:
-            num_patches: 200
+    - beamform
     - envelope_detect
     -   name: normalize
         params:

oslo/A_cardiac/README.md

@@ -1,124 +1,124 @@
----
-pretty_name: "USTB - In-vivo Cardiac (Verasonics P4-2)"
-license: cc-by-4.0
-task_categories:
-  - image-to-image
-language:
-  - en
-tags:
-  - ultrasound
-  - rf
-  - openh-rf
-  - cardiac
-  - in-vivo
-size_categories:
-  - n<1K
----
-
-# USTB - In-vivo Cardiac (Verasonics P4-2)
-
-Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** contributed to the
-[OpenH-RF](https://github.com/open-h/OpenH-RF) initiative. All acquisitions are stored in the
-*zea* HDF5 file format (zea_version 0.1.6) and contain raw pre-beamformed
-channel data (`/data/raw_data`).
-
-## Dataset Description
-
-In-vivo human cardiac channel-capture data acquired with a Verasonics Vantage 256 research scanner and a P4-2 phased-array probe. The collection contains parasternal long-axis and apical four-chamber views recorded with focused transmit beams (sector scan). The data is pre-beamformed RF channel data intended for research into generalized beamforming, adaptive imaging and cardiac reconstruction.
-
-## Dataset Contributors
-
-University of Oslo (UiO), Department of Informatics. Primary contact: Ole Marius Hoel Rindal (omrindal@ifi.uio.no). Team: Ole Marius Hoel Rindal, Yucel Karabiyik, Sven Peter Nasholm, Andreas Austeng.
-
-## Dataset Creation Date
-
-06/23/2026 (packaging date; original acquisitions/simulations were produced between 2016 and 2023).
-
-## License / Terms of Use
-
-Released under **Creative Commons Attribution 4.0 International (CC BY 4.0)** — see the `LICENCE`
-file at the submission root (this license is also declared in the YAML frontmatter above). The
-contributed data is cleared for this license. The UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo. Contributed to OpenH-RF. Zenodo record 20261898.
-
-## 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. (OpenH-RF RFP task 6.1 Generalized Reconstruction).
-
-## Dataset Characterization
-
-- **Data Collection Method:** clinical
-- **Labeling Method:** N/A (raw channel data; no annotations).
-- **Acquisition system:** probe(s) P4-2;
-  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).
-
-## Dataset Format
-
-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:** 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
-- **Train / validation / test split:** not predefined (research dataset).
-- **Stored HDF5 size:** 3.53 GB (3,533,897,728 bytes).
-
-Per-acquisition summary:
-
-| Acquisition | frames | transmits | samples | elements | n_ch | fs (MHz) | fc (MHz) | size (MB) |
-|---|---|---|---|---|---|---|---|---|
-| `Verasonics_P2-4_apical_four_chamber_subject_1` | 25 | 101 | 2176 | 64 | 1 | 11.9 | 2.98 | 1181.42 |
-| `Verasonics_P2-4_parasternal_long_subject_1` | 50 | 101 | 2176 | 64 | 1 | 11.9 | 2.98 | 2352.48 |
-
-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
-
-Healthy adult volunteer(s). Anatomy: heart (parasternal long-axis, apical four-chamber). Scanner: Verasonics Vantage 256. Probe: P4-2 phased array (64 elements). Aggregate only; no per-subject identifiers are stored.
-
-## Data Validation
-
-A Delay-And-Sum `zea.Pipeline` is provided in the **`pipeline.yaml` at the submission root** and
-run by the single **`reconstruct.py` at the submission root**:
-`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`
-(RF is demodulated in-pipeline; IQ uses a baseband pipeline). The script is geometry-driven and
-recurses into every sub-dataset folder; running `python reconstruct.py` from the root reconstructs
-every `.hdf5` in the collection (or pass a folder-qualified path for a single acquisition) and writes
-`<name>_zea_bmode.png` next to each file as a portable check that the recorded geometry and timing
-are correct.
-
-The reference B-mode images committed alongside the data (`<name>_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
-
-Focused sector acquisition: lateral resolution and field of view follow the transmit geometry. Phased-array data is best reconstructed on a polar (sector) grid. Frame counts vary per acquisition.
-
-## Ethical Considerations
-
-In-vivo data recorded from healthy adult volunteers at the University of Oslo with written informed consent for research use and data sharing, and approval from the Regional Committee for Medical and Health Research Ethics (REK), Norway. The files contain only backscattered RF channel data and acquisition parameters - no patient name, identifier, date of birth, acquisition date, facial image, or any other HHS Safe Harbor identifier is present (de-identified by construction).
+---
+pretty_name: "USTB - In-vivo Cardiac (Verasonics P4-2)"
+license: cc-by-4.0
+task_categories:
+  - image-to-image
+language:
+  - en
+tags:
+  - ultrasound
+  - rf
+  - openh-rf
+  - cardiac
+  - in-vivo
+size_categories:
+  - n<1K
+---
+
+# USTB - In-vivo Cardiac (Verasonics P4-2)
+
+Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** contributed to the
+[OpenH-RF](https://github.com/open-h/OpenH-RF) initiative. All acquisitions are stored in the
+*zea* HDF5 file format (zea_version 0.1.6) and contain raw pre-beamformed
+channel data (`/data/raw_data`).
+
+## Dataset Description
+
+In-vivo human cardiac channel-capture data acquired with a Verasonics Vantage 256 research scanner and a P4-2 phased-array probe. The collection contains parasternal long-axis and apical four-chamber views recorded with focused transmit beams (sector scan). The data is pre-beamformed RF channel data intended for research into generalized beamforming, adaptive imaging and cardiac reconstruction.
+
+## Dataset Contributors
+
+University of Oslo (UiO), Department of Informatics. Primary contact: Ole Marius Hoel Rindal (omrindal@ifi.uio.no). Team: Ole Marius Hoel Rindal, Yucel Karabiyik, Sven Peter Nasholm, Andreas Austeng.
+
+## Dataset Creation Date
+
+06/23/2026 (packaging date; original acquisitions/simulations were produced between 2016 and 2023).
+
+## License / Terms of Use
+
+Released under **Creative Commons Attribution 4.0 International (CC BY 4.0)** — see the `LICENCE`
+file at the submission root (this license is also declared in the YAML frontmatter above). The
+contributed data is cleared for this license. The UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo. Contributed to OpenH-RF. Zenodo record 20261898.
+
+## 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. (OpenH-RF RFP task 6.1 Generalized Reconstruction).
+
+## Dataset Characterization
+
+- **Data Collection Method:** clinical
+- **Labeling Method:** N/A (raw channel data; no annotations).
+- **Acquisition system:** probe(s) P4-2;
+  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).
+
+## Dataset Format
+
+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:** 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
+- **Train / validation / test split:** not predefined (research dataset).
+- **Stored HDF5 size:** 3.53 GB (3,533,897,728 bytes).
+
+Per-acquisition summary:
+
+| Acquisition | frames | transmits | samples | elements | n_ch | fs (MHz) | fc (MHz) | size (MB) |
+|---|---|---|---|---|---|---|---|---|
+| `Verasonics_P2-4_apical_four_chamber_subject_1` | 25 | 101 | 2176 | 64 | 1 | 11.9 | 2.98 | 1181.42 |
+| `Verasonics_P2-4_parasternal_long_subject_1` | 50 | 101 | 2176 | 64 | 1 | 11.9 | 2.98 | 2352.48 |
+
+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
+
+Healthy adult volunteer(s). Anatomy: heart (parasternal long-axis, apical four-chamber). Scanner: Verasonics Vantage 256. Probe: P4-2 phased array (64 elements). Aggregate only; no per-subject identifiers are stored.
+
+## Data Validation
+
+A Delay-And-Sum `zea.Pipeline` is provided in the **`pipeline.yaml` at the submission root** and
+run by the single **`reconstruct.py` at the submission root**:
+`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`
+(RF is demodulated in-pipeline; IQ uses a baseband pipeline). The script is geometry-driven and
+recurses into every sub-dataset folder; running `python reconstruct.py` from the root reconstructs
+every `.hdf5` in the collection (or pass a folder-qualified path for a single acquisition) and writes
+`<name>_zea_bmode.png` next to each file as a portable check that the recorded geometry and timing
+are correct.
+
+The reference B-mode images committed alongside the data (`<name>_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
+
+Focused sector acquisition: lateral resolution and field of view follow the transmit geometry. Phased-array data is best reconstructed on a polar (sector) grid. Frame counts vary per acquisition.
+
+## Ethical Considerations
+
+In-vivo data recorded from healthy adult volunteers at the University of Oslo with written informed consent for research use and data sharing, and approval from the Regional Committee for Medical and Health Research Ethics (REK), Norway. The files contain only backscattered RF channel data and acquisition parameters - no patient name, identifier, date of birth, acquisition date, facial image, or any other HHS Safe Harbor identifier is present (de-identified by construction).

oslo/B_carotid/README.md

@@ -1,126 +1,126 @@
----
-pretty_name: "USTB - In-vivo Carotid (Verasonics L7-4)"
-license: cc-by-4.0
-task_categories:
-  - image-to-image
-language:
-  - en
-tags:
-  - ultrasound
-  - rf
-  - openh-rf
-  - vascular
-  - carotid
-  - in-vivo
-size_categories:
-  - n<1K
----
-
-# USTB - In-vivo Carotid (Verasonics L7-4)
-
-Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** contributed to the
-[OpenH-RF](https://github.com/open-h/OpenH-RF) initiative. All acquisitions are stored in the
-*zea* HDF5 file format (zea_version 0.1.6) and contain raw pre-beamformed
-channel data (`/data/raw_data`).
-
-## Dataset Description
-
-In-vivo human carotid-artery channel-capture data acquired with a Verasonics Vantage 256 and an L7-4 linear-array probe, cross-sectional views, focused transmit imaging. Pre-beamformed RF channel data for vascular imaging and beamforming research.
-
-## Dataset Contributors
-
-University of Oslo (UiO), Department of Informatics. Primary contact: Ole Marius Hoel Rindal (omrindal@ifi.uio.no). Team: Ole Marius Hoel Rindal, Yucel Karabiyik, Sven Peter Nasholm, Andreas Austeng.
-
-## Dataset Creation Date
-
-06/23/2026 (packaging date; original acquisitions/simulations were produced between 2016 and 2023).
-
-## License / Terms of Use
-
-Released under **Creative Commons Attribution 4.0 International (CC BY 4.0)** — see the `LICENCE`
-file at the submission root (this license is also declared in the YAML frontmatter above). The
-contributed data is cleared for this license. The UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo. Contributed to OpenH-RF. Zenodo record 20261898.
-
-## Intended Usage
-
-Generalized reconstruction and adaptive beamforming of vascular ultrasound (RFP task 6.1). (OpenH-RF RFP task 6.1 Generalized Reconstruction).
-
-## Dataset Characterization
-
-- **Data Collection Method:** clinical
-- **Labeling Method:** N/A (raw channel data; no annotations).
-- **Acquisition system:** probe(s) L7-4;
-  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).
-
-## Dataset Format
-
-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:** 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
-- **Train / validation / test split:** not predefined (research dataset).
-- **Stored HDF5 size:** 219.48 MB (219,480,064 bytes).
-
-Per-acquisition summary:
-
-| Acquisition | frames | transmits | samples | elements | n_ch | fs (MHz) | fc (MHz) | size (MB) |
-|---|---|---|---|---|---|---|---|---|
-| `L7_FI_carotid_cross_1` | 2 | 128 | 1920 | 128 | 1 | 20.8 | 5.21 | 82.84 |
-| `L7_FI_carotid_cross_2` | 2 | 128 | 1920 | 128 | 1 | 20.8 | 5.21 | 66.98 |
-| `L7_FI_carotid_cross_sub_2` | 2 | 128 | 1920 | 128 | 1 | 20.8 | 5.21 | 69.66 |
-
-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
-
-Healthy adult volunteer(s). Anatomy: carotid artery (cross-section). Scanner: Verasonics Vantage 256. Probe: L7-4 linear array (128 elements). Aggregate only; no per-subject identifiers.
-
-## Data Validation
-
-A Delay-And-Sum `zea.Pipeline` is provided in the **`pipeline.yaml` at the submission root** and
-run by the single **`reconstruct.py` at the submission root**:
-`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`
-(RF is demodulated in-pipeline; IQ uses a baseband pipeline). The script is geometry-driven and
-recurses into every sub-dataset folder; running `python reconstruct.py` from the root reconstructs
-every `.hdf5` in the collection (or pass a folder-qualified path for a single acquisition) and writes
-`<name>_zea_bmode.png` next to each file as a portable check that the recorded geometry and timing
-are correct.
-
-The reference B-mode images committed alongside the data (`<name>_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/few-frame acquisitions; focused linear imaging.
-
-## Ethical Considerations
-
-In-vivo data recorded from healthy adult volunteers at the University of Oslo with written informed consent for research use and data sharing, and approval from the Regional Committee for Medical and Health Research Ethics (REK), Norway. Files contain only RF channel data and acquisition parameters; no HHS Safe Harbor identifiers are present.
+---
+pretty_name: "USTB - In-vivo Carotid (Verasonics L7-4)"
+license: cc-by-4.0
+task_categories:
+  - image-to-image
+language:
+  - en
+tags:
+  - ultrasound
+  - rf
+  - openh-rf
+  - vascular
+  - carotid
+  - in-vivo
+size_categories:
+  - n<1K
+---
+
+# USTB - In-vivo Carotid (Verasonics L7-4)
+
+Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** contributed to the
+[OpenH-RF](https://github.com/open-h/OpenH-RF) initiative. All acquisitions are stored in the
+*zea* HDF5 file format (zea_version 0.1.6) and contain raw pre-beamformed
+channel data (`/data/raw_data`).
+
+## Dataset Description
+
+In-vivo human carotid-artery channel-capture data acquired with a Verasonics Vantage 256 and an L7-4 linear-array probe, cross-sectional views, focused transmit imaging. Pre-beamformed RF channel data for vascular imaging and beamforming research.
+
+## Dataset Contributors
+
+University of Oslo (UiO), Department of Informatics. Primary contact: Ole Marius Hoel Rindal (omrindal@ifi.uio.no). Team: Ole Marius Hoel Rindal, Yucel Karabiyik, Sven Peter Nasholm, Andreas Austeng.
+
+## Dataset Creation Date
+
+06/23/2026 (packaging date; original acquisitions/simulations were produced between 2016 and 2023).
+
+## License / Terms of Use
+
+Released under **Creative Commons Attribution 4.0 International (CC BY 4.0)** — see the `LICENCE`
+file at the submission root (this license is also declared in the YAML frontmatter above). The
+contributed data is cleared for this license. The UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo. Contributed to OpenH-RF. Zenodo record 20261898.
+
+## Intended Usage
+
+Generalized reconstruction and adaptive beamforming of vascular ultrasound (RFP task 6.1). (OpenH-RF RFP task 6.1 Generalized Reconstruction).
+
+## Dataset Characterization
+
+- **Data Collection Method:** clinical
+- **Labeling Method:** N/A (raw channel data; no annotations).
+- **Acquisition system:** probe(s) L7-4;
+  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).
+
+## Dataset Format
+
+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:** 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
+- **Train / validation / test split:** not predefined (research dataset).
+- **Stored HDF5 size:** 219.48 MB (219,480,064 bytes).
+
+Per-acquisition summary:
+
+| Acquisition | frames | transmits | samples | elements | n_ch | fs (MHz) | fc (MHz) | size (MB) |
+|---|---|---|---|---|---|---|---|---|
+| `L7_FI_carotid_cross_1` | 2 | 128 | 1920 | 128 | 1 | 20.8 | 5.21 | 82.84 |
+| `L7_FI_carotid_cross_2` | 2 | 128 | 1920 | 128 | 1 | 20.8 | 5.21 | 66.98 |
+| `L7_FI_carotid_cross_sub_2` | 2 | 128 | 1920 | 128 | 1 | 20.8 | 5.21 | 69.66 |
+
+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
+
+Healthy adult volunteer(s). Anatomy: carotid artery (cross-section). Scanner: Verasonics Vantage 256. Probe: L7-4 linear array (128 elements). Aggregate only; no per-subject identifiers.
+
+## Data Validation
+
+A Delay-And-Sum `zea.Pipeline` is provided in the **`pipeline.yaml` at the submission root** and
+run by the single **`reconstruct.py` at the submission root**:
+`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`
+(RF is demodulated in-pipeline; IQ uses a baseband pipeline). The script is geometry-driven and
+recurses into every sub-dataset folder; running `python reconstruct.py` from the root reconstructs
+every `.hdf5` in the collection (or pass a folder-qualified path for a single acquisition) and writes
+`<name>_zea_bmode.png` next to each file as a portable check that the recorded geometry and timing
+are correct.
+
+The reference B-mode images committed alongside the data (`<name>_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/few-frame acquisitions; focused linear imaging.
+
+## Ethical Considerations
+
+In-vivo data recorded from healthy adult volunteers at the University of Oslo with written informed consent for research use and data sharing, and approval from the Regional Committee for Medical and Health Research Ethics (REK), Norway. Files contain only RF channel data and acquisition parameters; no HHS Safe Harbor identifiers are present.

oslo/C_verasonics_phantom/README.md

@@ -1,137 +1,137 @@
----
-pretty_name: "USTB - Phantom (Verasonics L7-4 / P4)"
-license: cc-by-4.0
-task_categories:
-  - image-to-image
-language:
-  - en
-tags:
-  - ultrasound
-  - rf
-  - openh-rf
-  - phantom
-  - cirs
-size_categories:
-  - n<1K
----
-
-# USTB - Phantom (Verasonics L7-4 / P4)
-
-Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** contributed to the
-[OpenH-RF](https://github.com/open-h/OpenH-RF) initiative. All acquisitions are stored in the
-*zea* HDF5 file format (zea_version 0.1.6) and contain raw pre-beamformed
-channel data (`/data/raw_data`).
-
-## Dataset Description
-
-Tissue-mimicking and table-top phantom channel-capture data acquired on a Verasonics Vantage 256 with L7-4 linear and P4 phased-array probes. The collection spans coherent plane-wave compounding (CPWC), focused imaging (FI), synthetic transmit aperture (STA) and diverging-wave (DW) sequences for resolution, contrast, dynamic-range and point-spread-function evaluation, including CIRS tissue-mimicking phantom targets.
-
-## Dataset Contributors
-
-University of Oslo (UiO), Department of Informatics. Primary contact: Ole Marius Hoel Rindal (omrindal@ifi.uio.no). Team: Ole Marius Hoel Rindal, Yucel Karabiyik, Sven Peter Nasholm, Andreas Austeng.
-
-## Dataset Creation Date
-
-06/23/2026 (packaging date; original acquisitions/simulations were produced between 2016 and 2023).
-
-## License / Terms of Use
-
-Released under **Creative Commons Attribution 4.0 International (CC BY 4.0)** — see the `LICENCE`
-file at the submission root (this license is also declared in the YAML frontmatter above). The
-contributed data is cleared for this license. The UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo. Contributed to OpenH-RF. Zenodo record 20261898.
-
-## 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. (OpenH-RF RFP task 6.1 Generalized Reconstruction).
-
-## Dataset Characterization
-
-- **Data Collection Method:** phantom
-- **Labeling Method:** Derived (known phantom geometry, e.g. CIRS Model 040GSE where applicable).
-- **Acquisition system:** probe(s) L7-4, P4-1, P4-2;
-  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).
-
-## Dataset Format
-
-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:** 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
-- **Train / validation / test split:** not predefined (research dataset).
-- **Stored HDF5 size:** 1.06 GB (1,058,734,080 bytes).
-
-Per-acquisition summary:
-
-| Acquisition | frames | transmits | samples | elements | n_ch | fs (MHz) | fc (MHz) | size (MB) |
-|---|---|---|---|---|---|---|---|---|
-| `experimental_dynamic_range_phantom` | 1 | 128 | 3840 | 128 | 1 | 40.8 | 5.00 | 218.23 |
-| `experimental_STAI_dynamic_range` | 1 | 128 | 1920 | 128 | 1 | 20.4 | 5.00 | 109.38 |
-| `FI_P4_cysts_center` | 1 | 128 | 2048 | 64 | 1 | 10.9 | 2.72 | 22.09 |
-| `FI_P4_point_scatterers` | 6 | 128 | 1792 | 64 | 1 | 10.9 | 2.72 | 115.74 |
-| `L7_CPWC_193328` | 3 | 15 | 1920 | 128 | 1 | 20.8 | 5.20 | 27.00 |
-| `L7_CPWC_TheGB` | 1 | 11 | 1920 | 128 | 1 | 20.8 | 5.00 | 4.39 |
-| `L7_DW_TheGB` | 1 | 25 | 1920 | 128 | 1 | 20.8 | 5.21 | 8.13 |
-| `L7_FI_IUS2018` | 1 | 128 | 1920 | 128 | 1 | 20.8 | 5.21 | 32.44 |
-| `L7_FI_TheGB` | 1 | 128 | 1920 | 128 | 1 | 20.8 | 5.21 | 30.34 |
-| `L7_FI_Verasonics` | 1 | 128 | 1920 | 128 | 1 | 20.8 | 5.21 | 110.23 |
-| `L7_FI_Verasonics_CIRS` | 1 | 128 | 1920 | 128 | 1 | 20.8 | 5.21 | 109.90 |
-| `L7_FI_Verasonics_CIRS_points` | 1 | 128 | 1920 | 128 | 1 | 20.8 | 5.21 | 110.23 |
-| `L7_STA_TheGB` | 1 | 128 | 1920 | 128 | 1 | 20.8 | 5.00 | 28.97 |
-| `P4_FI_121444_45mm_focus` | 6 | 128 | 1280 | 64 | 1 | 11.9 | 2.98 | 94.24 |
-| `STAI_UFF_CIRS_phantom` | 1 | 128 | 2688 | 128 | 1 | 20.8 | 5.00 | 37.42 |
-
-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: CIRS tissue-mimicking phantoms and lab phantoms (wire/point targets, hypo/hyperechoic inclusions, dynamic-range targets). Probes: L7-4 (128 el.), P4 phased array.
-
-## Data Validation
-
-A Delay-And-Sum `zea.Pipeline` is provided in the **`pipeline.yaml` at the submission root** and
-run by the single **`reconstruct.py` at the submission root**:
-`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`
-(RF is demodulated in-pipeline; IQ uses a baseband pipeline). The script is geometry-driven and
-recurses into every sub-dataset folder; running `python reconstruct.py` from the root reconstructs
-every `.hdf5` in the collection (or pass a folder-qualified path for a single acquisition) and writes
-`<name>_zea_bmode.png` next to each file as a portable check that the recorded geometry and timing
-are correct.
-
-The reference B-mode images committed alongside the data (`<name>_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
-
-Mixed transmit schemes across files (CPWC / FI / STA / DW). Single-frame acquisitions for most phantoms. The reference reconstruction uses a single B-mode pipeline; per-scheme tuning may improve image quality.
-
-## Ethical Considerations
-
-Phantom / table-top acquisitions; no human or animal subjects are involved. No ethical considerations beyond standard laboratory practice.
+---
+pretty_name: "USTB - Phantom (Verasonics L7-4 / P4)"
+license: cc-by-4.0
+task_categories:
+  - image-to-image
+language:
+  - en
+tags:
+  - ultrasound
+  - rf
+  - openh-rf
+  - phantom
+  - cirs
+size_categories:
+  - n<1K
+---
+
+# USTB - Phantom (Verasonics L7-4 / P4)
+
+Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** contributed to the
+[OpenH-RF](https://github.com/open-h/OpenH-RF) initiative. All acquisitions are stored in the
+*zea* HDF5 file format (zea_version 0.1.6) and contain raw pre-beamformed
+channel data (`/data/raw_data`).
+
+## Dataset Description
+
+Tissue-mimicking and table-top phantom channel-capture data acquired on a Verasonics Vantage 256 with L7-4 linear and P4 phased-array probes. The collection spans coherent plane-wave compounding (CPWC), focused imaging (FI), synthetic transmit aperture (STA) and diverging-wave (DW) sequences for resolution, contrast, dynamic-range and point-spread-function evaluation, including CIRS tissue-mimicking phantom targets.
+
+## Dataset Contributors
+
+University of Oslo (UiO), Department of Informatics. Primary contact: Ole Marius Hoel Rindal (omrindal@ifi.uio.no). Team: Ole Marius Hoel Rindal, Yucel Karabiyik, Sven Peter Nasholm, Andreas Austeng.
+
+## Dataset Creation Date
+
+06/23/2026 (packaging date; original acquisitions/simulations were produced between 2016 and 2023).
+
+## License / Terms of Use
+
+Released under **Creative Commons Attribution 4.0 International (CC BY 4.0)** — see the `LICENCE`
+file at the submission root (this license is also declared in the YAML frontmatter above). The
+contributed data is cleared for this license. The UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo. Contributed to OpenH-RF. Zenodo record 20261898.
+
+## 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. (OpenH-RF RFP task 6.1 Generalized Reconstruction).
+
+## Dataset Characterization
+
+- **Data Collection Method:** phantom
+- **Labeling Method:** Derived (known phantom geometry, e.g. CIRS Model 040GSE where applicable).
+- **Acquisition system:** probe(s) L7-4, P4-1, P4-2;
+  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).
+
+## Dataset Format
+
+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:** 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
+- **Train / validation / test split:** not predefined (research dataset).
+- **Stored HDF5 size:** 1.06 GB (1,058,734,080 bytes).
+
+Per-acquisition summary:
+
+| Acquisition | frames | transmits | samples | elements | n_ch | fs (MHz) | fc (MHz) | size (MB) |
+|---|---|---|---|---|---|---|---|---|
+| `experimental_dynamic_range_phantom` | 1 | 128 | 3840 | 128 | 1 | 40.8 | 5.00 | 218.23 |
+| `experimental_STAI_dynamic_range` | 1 | 128 | 1920 | 128 | 1 | 20.4 | 5.00 | 109.38 |
+| `FI_P4_cysts_center` | 1 | 128 | 2048 | 64 | 1 | 10.9 | 2.72 | 22.09 |
+| `FI_P4_point_scatterers` | 6 | 128 | 1792 | 64 | 1 | 10.9 | 2.72 | 115.74 |
+| `L7_CPWC_193328` | 3 | 15 | 1920 | 128 | 1 | 20.8 | 5.20 | 27.00 |
+| `L7_CPWC_TheGB` | 1 | 11 | 1920 | 128 | 1 | 20.8 | 5.00 | 4.39 |
+| `L7_DW_TheGB` | 1 | 25 | 1920 | 128 | 1 | 20.8 | 5.21 | 8.13 |
+| `L7_FI_IUS2018` | 1 | 128 | 1920 | 128 | 1 | 20.8 | 5.21 | 32.44 |
+| `L7_FI_TheGB` | 1 | 128 | 1920 | 128 | 1 | 20.8 | 5.21 | 30.34 |
+| `L7_FI_Verasonics` | 1 | 128 | 1920 | 128 | 1 | 20.8 | 5.21 | 110.23 |
+| `L7_FI_Verasonics_CIRS` | 1 | 128 | 1920 | 128 | 1 | 20.8 | 5.21 | 109.90 |
+| `L7_FI_Verasonics_CIRS_points` | 1 | 128 | 1920 | 128 | 1 | 20.8 | 5.21 | 110.23 |
+| `L7_STA_TheGB` | 1 | 128 | 1920 | 128 | 1 | 20.8 | 5.00 | 28.97 |
+| `P4_FI_121444_45mm_focus` | 6 | 128 | 1280 | 64 | 1 | 11.9 | 2.98 | 94.24 |
+| `STAI_UFF_CIRS_phantom` | 1 | 128 | 2688 | 128 | 1 | 20.8 | 5.00 | 37.42 |
+
+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: CIRS tissue-mimicking phantoms and lab phantoms (wire/point targets, hypo/hyperechoic inclusions, dynamic-range targets). Probes: L7-4 (128 el.), P4 phased array.
+
+## Data Validation
+
+A Delay-And-Sum `zea.Pipeline` is provided in the **`pipeline.yaml` at the submission root** and
+run by the single **`reconstruct.py` at the submission root**:
+`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`
+(RF is demodulated in-pipeline; IQ uses a baseband pipeline). The script is geometry-driven and
+recurses into every sub-dataset folder; running `python reconstruct.py` from the root reconstructs
+every `.hdf5` in the collection (or pass a folder-qualified path for a single acquisition) and writes
+`<name>_zea_bmode.png` next to each file as a portable check that the recorded geometry and timing
+are correct.
+
+The reference B-mode images committed alongside the data (`<name>_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
+
+Mixed transmit schemes across files (CPWC / FI / STA / DW). Single-frame acquisitions for most phantoms. The reference reconstruction uses a single B-mode pipeline; per-scheme tuning may improve image quality.
+
+## Ethical Considerations
+
+Phantom / table-top acquisitions; no human or animal subjects are involved. No ethical considerations beyond standard laboratory practice.

oslo/D_alpinion_phantom/README.md

@@ -1,126 +1,126 @@
----
-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)
-
-Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** contributed to the
-[OpenH-RF](https://github.com/open-h/OpenH-RF) initiative. All acquisitions are stored in the
-*zea* HDF5 file format (zea_version 0.1.6) and contain raw pre-beamformed
-channel data (`/data/raw_data`).
-
-## Dataset Description
-
-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 Contributors
-
-University of Oslo (UiO), Department of Informatics. Primary contact: Ole Marius Hoel Rindal (omrindal@ifi.uio.no). Team: Ole Marius Hoel Rindal, Yucel Karabiyik, Sven Peter Nasholm, Andreas Austeng.
-
-## Dataset Creation Date
-
-06/23/2026 (packaging date; original acquisitions/simulations were produced between 2016 and 2023).
-
-## License / Terms of Use
-
-Released under **Creative Commons Attribution 4.0 International (CC BY 4.0)** — see the `LICENCE`
-file at the submission root (this license is also declared in the YAML frontmatter above). The
-contributed data is cleared for this license. The UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo. Contributed to OpenH-RF. Zenodo record 20261898.
-
-## 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).
-
-## Dataset Format
-
-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` is provided in the **`pipeline.yaml` at the submission root** and
-run by the single **`reconstruct.py` at the submission root**:
-`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`
-(RF is demodulated in-pipeline; IQ uses a baseband pipeline). The script is geometry-driven and
-recurses into every sub-dataset folder; running `python reconstruct.py` from the root reconstructs
-every `.hdf5` in the collection (or pass a folder-qualified path for a single acquisition) and writes
-`<name>_zea_bmode.png` next to each file as a portable check that the recorded geometry and timing
-are correct.
-
-The reference B-mode images committed alongside the data (`<name>_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.
+---
+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)
+
+Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** contributed to the
+[OpenH-RF](https://github.com/open-h/OpenH-RF) initiative. All acquisitions are stored in the
+*zea* HDF5 file format (zea_version 0.1.6) and contain raw pre-beamformed
+channel data (`/data/raw_data`).
+
+## Dataset Description
+
+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 Contributors
+
+University of Oslo (UiO), Department of Informatics. Primary contact: Ole Marius Hoel Rindal (omrindal@ifi.uio.no). Team: Ole Marius Hoel Rindal, Yucel Karabiyik, Sven Peter Nasholm, Andreas Austeng.
+
+## Dataset Creation Date
+
+06/23/2026 (packaging date; original acquisitions/simulations were produced between 2016 and 2023).
+
+## License / Terms of Use
+
+Released under **Creative Commons Attribution 4.0 International (CC BY 4.0)** — see the `LICENCE`
+file at the submission root (this license is also declared in the YAML frontmatter above). The
+contributed data is cleared for this license. The UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo. Contributed to OpenH-RF. Zenodo record 20261898.
+
+## 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).
+
+## Dataset Format
+
+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` is provided in the **`pipeline.yaml` at the submission root** and
+run by the single **`reconstruct.py` at the submission root**:
+`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`
+(RF is demodulated in-pipeline; IQ uses a baseband pipeline). The script is geometry-driven and
+recurses into every sub-dataset folder; running `python reconstruct.py` from the root reconstructs
+every `.hdf5` in the collection (or pass a folder-qualified path for a single acquisition) and writes
+`<name>_zea_bmode.png` next to each file as a portable check that the recorded geometry and timing
+are correct.
+
+The reference B-mode images committed alongside the data (`<name>_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.

oslo/E_simulation/README.md

@@ -1,134 +1,134 @@
----
-pretty_name: "USTB - Simulation (Field II)"
-license: cc-by-4.0
-task_categories:
-  - image-to-image
-language:
-  - en
-tags:
-  - ultrasound
-  - rf
-  - openh-rf
-  - simulation
-  - field-ii
-  - synthetic
-size_categories:
-  - n<1K
----
-
-# USTB - Simulation (Field II)
-
-Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** contributed to the
-[OpenH-RF](https://github.com/open-h/OpenH-RF) initiative. All acquisitions are stored in the
-*zea* HDF5 file format (zea_version 0.1.6) and contain raw pre-beamformed
-channel data (`/data/raw_data`).
-
-## Dataset Description
-
-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 Contributors
-
-University of Oslo (UiO), Department of Informatics. Primary contact: Ole Marius Hoel Rindal (omrindal@ifi.uio.no). Team: Ole Marius Hoel Rindal, Yucel Karabiyik, Sven Peter Nasholm, Andreas Austeng.
-
-## Dataset Creation Date
-
-06/23/2026 (packaging date; original acquisitions/simulations were produced between 2016 and 2023).
-
-## License / Terms of Use
-
-Released under **Creative Commons Attribution 4.0 International (CC BY 4.0)** — see the `LICENCE`
-file at the submission root (this license is also declared in the YAML frontmatter above). The
-contributed data is cleared for this license. The UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo. Contributed to OpenH-RF. Zenodo record 20261898.
-
-## 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. (OpenH-RF RFP task 6.1 Generalized Reconstruction).
-
-## Dataset Characterization
-
-- **Data Collection Method:** synthetic
-- **Labeling Method:** Synthetic ground truth (known scatterer positions and medium parameters).
-- **Acquisition system:** probe(s) L7-4, P4-1;
-  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).
-
-## Dataset Format
-
-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/IQ (n_ch in [1, 2]).
-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:** 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
-- **Train / validation / test split:** not predefined (research dataset).
-- **Stored HDF5 size:** 2.98 GB (2,980,642,816 bytes).
-
-Per-acquisition summary:
-
-| Acquisition | frames | transmits | samples | elements | n_ch | fs (MHz) | fc (MHz) | size (MB) |
-|---|---|---|---|---|---|---|---|---|
-| `FieldII_CPWC_point_scatterers_res_v2` | 7 | 1 | 7792 | 128 | 1 | 100.0 | 5.16 | 2.10 |
-| `FieldII_CPWC_simulation_v2` | 1 | 1 | 6494 | 128 | 1 | 100.0 | 5.13 | 0.92 |
-| `FieldII_P4_point_scatterers` | 1 | 128 | 14349 | 64 | 1 | 100.0 | 2.56 | 406.13 |
-| `FieldII_speckle_DMASsimulation300000pts` | 1 | 96 | 10570 | 128 | 1 | 100.0 | 3.50 | 200.67 |
-| `FieldII_STAI_dynamic_range` | 1 | 128 | 7792 | 128 | 1 | 100.0 | 5.13 | 355.40 |
-| `FieldII_STAI_simulated_dynamic_range` | 1 | 128 | 7792 | 128 | 1 | 100.0 | 5.13 | 347.60 |
-| `FieldII_STAI_uniform_fov` | 1 | 128 | 2771 | 128 | 1 | 25.0 | 5.13 | 125.57 |
-| `PICMUS_numerical_calib_v2` | 1 | 5 | 445 | 128 | 2 | 5.2 | 5.21 | 2.16 |
-| `speckle_sim_FI_P4_probe_apod_1_speckle_long_many_angles` | 1 | 150 | 15786 | 64 | 1 | 100.0 | 2.56 | 506.40 |
-| `speckle_sim_FI_P4_probe_apod_2_speckle_long_many_angles` | 1 | 150 | 15786 | 64 | 1 | 100.0 | 2.56 | 512.49 |
-| `speckle_sim_FI_P4_probe_apod_3_speckle_long_many_angles` | 1 | 150 | 15786 | 64 | 1 | 100.0 | 2.56 | 521.21 |
-
-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. Synthetic media simulated with Field II. Virtual probes: L7-4-like linear and P4-like phased arrays.
-
-## Data Validation
-
-A Delay-And-Sum `zea.Pipeline` is provided in the **`pipeline.yaml` at the submission root** and
-run by the single **`reconstruct.py` at the submission root**:
-`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`
-(RF is demodulated in-pipeline; IQ uses a baseband pipeline). The script is geometry-driven and
-recurses into every sub-dataset folder; running `python reconstruct.py` from the root reconstructs
-every `.hdf5` in the collection (or pass a folder-qualified path for a single acquisition) and writes
-`<name>_zea_bmode.png` next to each file as a portable check that the recorded geometry and timing
-are correct.
-
-The reference B-mode images committed alongside the data (`<name>_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
-
-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
-
-Fully synthetic data generated with the Field II simulation framework; no human or animal subjects. No personal data is present. The included numerical calibration file was produced in collaboration with our group as part of the PICMUS effort (IEEE IUS 2016) and is released here under CC BY 4.0.
+---
+pretty_name: "USTB - Simulation (Field II)"
+license: cc-by-4.0
+task_categories:
+  - image-to-image
+language:
+  - en
+tags:
+  - ultrasound
+  - rf
+  - openh-rf
+  - simulation
+  - field-ii
+  - synthetic
+size_categories:
+  - n<1K
+---
+
+# USTB - Simulation (Field II)
+
+Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** contributed to the
+[OpenH-RF](https://github.com/open-h/OpenH-RF) initiative. All acquisitions are stored in the
+*zea* HDF5 file format (zea_version 0.1.6) and contain raw pre-beamformed
+channel data (`/data/raw_data`).
+
+## Dataset Description
+
+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 Contributors
+
+University of Oslo (UiO), Department of Informatics. Primary contact: Ole Marius Hoel Rindal (omrindal@ifi.uio.no). Team: Ole Marius Hoel Rindal, Yucel Karabiyik, Sven Peter Nasholm, Andreas Austeng.
+
+## Dataset Creation Date
+
+06/23/2026 (packaging date; original acquisitions/simulations were produced between 2016 and 2023).
+
+## License / Terms of Use
+
+Released under **Creative Commons Attribution 4.0 International (CC BY 4.0)** — see the `LICENCE`
+file at the submission root (this license is also declared in the YAML frontmatter above). The
+contributed data is cleared for this license. The UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo. Contributed to OpenH-RF. Zenodo record 20261898.
+
+## 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. (OpenH-RF RFP task 6.1 Generalized Reconstruction).
+
+## Dataset Characterization
+
+- **Data Collection Method:** synthetic
+- **Labeling Method:** Synthetic ground truth (known scatterer positions and medium parameters).
+- **Acquisition system:** probe(s) L7-4, P4-1;
+  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).
+
+## Dataset Format
+
+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/IQ (n_ch in [1, 2]).
+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:** 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
+- **Train / validation / test split:** not predefined (research dataset).
+- **Stored HDF5 size:** 2.98 GB (2,980,642,816 bytes).
+
+Per-acquisition summary:
+
+| Acquisition | frames | transmits | samples | elements | n_ch | fs (MHz) | fc (MHz) | size (MB) |
+|---|---|---|---|---|---|---|---|---|
+| `FieldII_CPWC_point_scatterers_res_v2` | 7 | 1 | 7792 | 128 | 1 | 100.0 | 5.16 | 2.10 |
+| `FieldII_CPWC_simulation_v2` | 1 | 1 | 6494 | 128 | 1 | 100.0 | 5.13 | 0.92 |
+| `FieldII_P4_point_scatterers` | 1 | 128 | 14349 | 64 | 1 | 100.0 | 2.56 | 406.13 |
+| `FieldII_speckle_DMASsimulation300000pts` | 1 | 96 | 10570 | 128 | 1 | 100.0 | 3.50 | 200.67 |
+| `FieldII_STAI_dynamic_range` | 1 | 128 | 7792 | 128 | 1 | 100.0 | 5.13 | 355.40 |
+| `FieldII_STAI_simulated_dynamic_range` | 1 | 128 | 7792 | 128 | 1 | 100.0 | 5.13 | 347.60 |
+| `FieldII_STAI_uniform_fov` | 1 | 128 | 2771 | 128 | 1 | 25.0 | 5.13 | 125.57 |
+| `PICMUS_numerical_calib_v2` | 1 | 5 | 445 | 128 | 2 | 5.2 | 5.21 | 2.16 |
+| `speckle_sim_FI_P4_probe_apod_1_speckle_long_many_angles` | 1 | 150 | 15786 | 64 | 1 | 100.0 | 2.56 | 506.40 |
+| `speckle_sim_FI_P4_probe_apod_2_speckle_long_many_angles` | 1 | 150 | 15786 | 64 | 1 | 100.0 | 2.56 | 512.49 |
+| `speckle_sim_FI_P4_probe_apod_3_speckle_long_many_angles` | 1 | 150 | 15786 | 64 | 1 | 100.0 | 2.56 | 521.21 |
+
+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. Synthetic media simulated with Field II. Virtual probes: L7-4-like linear and P4-like phased arrays.
+
+## Data Validation
+
+A Delay-And-Sum `zea.Pipeline` is provided in the **`pipeline.yaml` at the submission root** and
+run by the single **`reconstruct.py` at the submission root**:
+`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`
+(RF is demodulated in-pipeline; IQ uses a baseband pipeline). The script is geometry-driven and
+recurses into every sub-dataset folder; running `python reconstruct.py` from the root reconstructs
+every `.hdf5` in the collection (or pass a folder-qualified path for a single acquisition) and writes
+`<name>_zea_bmode.png` next to each file as a portable check that the recorded geometry and timing
+are correct.
+
+The reference B-mode images committed alongside the data (`<name>_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
+
+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
+
+Fully synthetic data generated with the Field II simulation framework; no human or animal subjects. No personal data is present. The included numerical calibration file was produced in collaboration with our group as part of the PICMUS effort (IEEE IUS 2016) and is released here under CC BY 4.0.

oslo/parameters.yaml

@@ -0,0 +1,217 @@
+# Per-acquisition reconstruction parameters, shared by every folder's
+# reconstruct.py. For each acquisition:
+#   pipeline      -- which reconstruction to use:
+#                    scanline (focused linear FI, per-beam),
+#                    sector   (phased/steered focused, pipeline_sector.yaml),
+#                    iq       (baseband IQ, pipeline_iq.yaml),
+#                    compound (non-focused linear, 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 (<name>_zea_refocus_bmode.png).
+#                    REFoCUS (Bottenus 2018) inverts the transmit-encoding matrix to
+#                    recover the multistatic (full-matrix-capture) dataset before DAS.
+#                    Enabled for every acquisition with enough encoded transmits to make
+#                    the inversion well posed (>= 8 transmit events). reconstruct.py picks
+#                    the polar pipeline_refocus_sector.yaml for `sector` acquisitions and
+#                    the linear/cartesian pipeline_refocus.yaml for everything else.
+#                    NOT enabled where REFoCUS is ill-posed or undefined:
+#                      * single/few-transmit acquisitions (plane-wave tracking with
+#                        n_tx=1, PICMUS with 5 angles),
+#                      * synthetic transmit aperture (STA) acquisitions, which are
+#                        already multistatic (nothing to decode).
+
+# A_cardiac
+Verasonics_P2-4_apical_four_chamber_subject_1:
+  pipeline: sector
+  refocus: true
+  zlims: [1, 110]
+Verasonics_P2-4_parasternal_long_subject_1:
+  pipeline: sector
+  refocus: true
+  zlims: [1, 110]
+
+# B_carotid
+L7_FI_carotid_cross_1:
+  pipeline: scanline
+  refocus: true
+  zlims: [1, 60]
+  xlims: [-19, 19]
+L7_FI_carotid_cross_2:
+  pipeline: scanline
+  refocus: true
+  zlims: [1, 60]
+  xlims: [-19, 19]
+L7_FI_carotid_cross_sub_2:
+  pipeline: scanline
+  refocus: true
+  zlims: [1, 60]
+  xlims: [-19, 19]
+
+# C_verasonics_phantom
+FI_P4_cysts_center:
+  pipeline: sector
+  refocus: true
+  zlims: [1, 115]
+FI_P4_point_scatterers:
+  pipeline: sector
+  refocus: true
+  zlims: [1, 115]
+L7_CPWC_193328:
+  pipeline: compound
+  refocus: true
+  zlims: [0, 50]
+  xlims: [-20, 20]
+L7_CPWC_TheGB:
+  pipeline: compound
+  refocus: true
+  zlims: [3, 45]
+  xlims: [-20, 20]
+L7_DW_TheGB:
+  pipeline: compound
+  refocus: true
+  zlims: [3, 50]
+  xlims: [-20, 20]
+L7_FI_IUS2018:
+  pipeline: scanline
+  refocus: true
+  zlims: [1, 60]
+  xlims: [-19, 19]
+L7_FI_TheGB:
+  pipeline: scanline
+  refocus: true
+  zlims: [1, 60]
+  xlims: [-19, 19]
+L7_FI_Verasonics:
+  pipeline: scanline
+  refocus: true
+  zlims: [1, 60]
+  xlims: [-19, 19]
+L7_FI_Verasonics_CIRS:
+  pipeline: scanline
+  refocus: true
+  zlims: [1, 60]
+  xlims: [-19, 19]
+L7_FI_Verasonics_CIRS_points:
+  pipeline: scanline
+  refocus: true
+  zlims: [1, 60]
+  xlims: [-19, 19]
+# STA (already multistatic -> REFoCUS N/A)
+L7_STA_TheGB:
+  pipeline: compound
+  zlims: [3, 45]
+  xlims: [-20, 20]
+P4_FI_121444_45mm_focus:
+  pipeline: sector
+  refocus: true
+  zlims: [1, 58]
+# STA (already multistatic -> REFoCUS N/A)
+STAI_UFF_CIRS_phantom:
+  pipeline: compound
+  zlims: [10, 80]
+  xlims: [-15, 15]
+# STA (already multistatic -> REFoCUS N/A)
+experimental_STAI_dynamic_range:
+  pipeline: compound
+  zlims: [3, 55]
+  xlims: [-19, 19]
+# STA (n_tx == n_el == 128, already multistatic -> REFoCUS N/A)
+experimental_dynamic_range_phantom:
+  pipeline: compound
+  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:
+  pipeline: compound
+  zlims: [35, 42]
+  xlims: [-3, 3]
+# Single plane wave (n_tx=1) -> REFoCUS N/A
+FieldII_CPWC_simulation_v2:
+  pipeline: compound
+  zlims: [3, 45]
+  xlims: [-20, 20]
+FieldII_P4_point_scatterers:
+  pipeline: sector
+  refocus: true
+  zlims: [1, 110]
+# STA (already multistatic -> REFoCUS N/A)
+FieldII_STAI_dynamic_range:
+  pipeline: compound
+  zlims: [5, 55]
+  xlims: [-20, 20]
+# STA (already multistatic -> REFoCUS N/A)
+FieldII_STAI_simulated_dynamic_range:
+  pipeline: compound
+  zlims: [5, 55]
+  xlims: [-20, 20]
+# STA (already multistatic -> REFoCUS N/A)
+FieldII_STAI_uniform_fov:
+  pipeline: compound
+  zlims: [1, 55]
+  xlims: [-20, 20]
+FieldII_speckle_DMASsimulation300000pts:
+  pipeline: sector
+  refocus: true
+  zlims: [1, 60]
+  xlims: [-14, 14]
+# Baseband IQ, only 5 plane-wave angles -> REFoCUS N/A
+PICMUS_numerical_calib_v2:
+  pipeline: iq
+  zlims: [5, 50]
+  xlims: [-19, 19]
+speckle_sim_FI_P4_probe_apod_1_speckle_long_many_angles:
+  pipeline: sector
+  refocus: true
+  zlims: [1, 120]
+speckle_sim_FI_P4_probe_apod_2_speckle_long_many_angles:
+  pipeline: sector
+  refocus: true
+  zlims: [1, 120]
+speckle_sim_FI_P4_probe_apod_3_speckle_long_many_angles:
+  pipeline: sector
+  refocus: true
+  zlims: [1, 120]
+
+# F_motion
+# Plane-wave tracking (n_tx=1 per frame) -> REFoCUS N/A
+ARFI_dataset:
+  pipeline: compound
+  zlims: [1, 40]
+  xlims: [-19, 19]
+SWE_L7_type_I:
+  pipeline: compound
+  zlims: [1, 40]
+  xlims: [-19, 19]
+SWE_L7_type_III:
+  pipeline: compound
+  zlims: [1, 40]
+  xlims: [-19, 19]
+SWE_L7_type_IV:
+  pipeline: compound
+  zlims: [1, 40]
+  xlims: [-19, 19]

oslo/pipeline.yaml

@@ -24,7 +24,6 @@ pipeline:
       params:
         beamformer: delay_and_sum
         enable_pfield: false
-        num_patches: 200
     - name: envelope_detect
     - name: normalize
     - name: log_compress

oslo/pipeline_iq.yaml

@@ -24,7 +24,6 @@ pipeline:
       params:
         beamformer: delay_and_sum
         enable_pfield: false
-        num_patches: 200
     - name: envelope_detect
     - name: normalize
     - name: log_compress

oslo/pipeline_refocus.yaml

@@ -37,7 +37,6 @@ pipeline:
       params:
         beamformer: delay_and_sum
         enable_pfield: true
-        num_patches: 200
     - name: envelope_detect
     - name: normalize
     - name: log_compress

oslo/pipeline_refocus_sector.yaml

@@ -46,7 +46,6 @@ pipeline:
       params:
         beamformer: delay_and_sum
         enable_pfield: true
-        num_patches: 200
     - name: envelope_detect
     - name: normalize
     - name: log_compress

oslo/pipeline_scanline.yaml

@@ -21,7 +21,6 @@ pipeline:
         beamformer: delay_and_sum
         enable_aligned_apodization: true
         enable_pfield: false
-        num_patches: 200
     - name: envelope_detect
     - name: normalize
     - name: log_compress

oslo/pipeline_sector.yaml

@@ -37,7 +37,6 @@ pipeline:
       params:
         beamformer: delay_and_sum
         enable_pfield: true
-        num_patches: 200
     - name: envelope_detect
     - name: normalize
     - name: log_compress

politorino/README.md

@@ -50,14 +50,13 @@ This data was employed for an initial study evaluating fascicle tracking algorit
 
 Submitted in the [`zea` file format](https://zea.readthedocs.io/en/latest/)
 (one HDF5 file per acquisition). 
-`visualize_tracking_on_image.py` maps the tracking data onto the reconstructed images. Options for running the file:
---file : .hdf5 file of the considered subjects 
---fps : frame rate of desired tracking (e.g., 25 fps) 
---tracking-frame-idx: index of the frame to visualize.
+`reconstruct.py` reconstructs a B-mode from the raw channel data and overlays
+the stored fascicle tracking on it, writing a `.png`. An example output is
+provided. The constants at the top of the script select what is drawn:
 
-The `reconstruct.py` code also implements the visualization of the tracking data and can be used stand-alone to view the tracking data. 
-
-The script automatically generates a .png file with the tracking data overlaid on the reconstructed image. An example output is provided. 
+- `FRAME` -- index of the acquisition frame to reconstruct
+- `FPS` -- which stored tracking rate to overlay (25, 50 or 125 fps)
+- `TRACK_INDEX` -- index of the tracking sample within that rate
 
 Per-sample contents of the HDF5:
 

politorino/pipeline.yaml

@@ -1,8 +1,6 @@
 pipeline:
     operations:
-    -   name: beamform
-        params:
-            num_patches: 200
+    - beamform
     - envelope_detect
     -   name: normalize
         params:

resolvestroke/clinical/SP02-Left-2/pipeline.yaml

@@ -15,7 +15,6 @@ pipeline:
     - name: beamform
       params:
         beamformer: delay_and_sum
-        num_patches: 100
     - name: envelope_detect
     - name: normalize
     - name: log_compress

resolvestroke/phantom_flow/pipeline.yaml

@@ -15,7 +15,6 @@ pipeline:
     - name: beamform
       params:
         beamformer: delay_and_sum
-        num_patches: 100
     - name: envelope_detect
     - name: normalize
     - name: log_compress

resolvestroke/phantom_mp/pipeline.yaml

@@ -15,7 +15,6 @@ pipeline:
     - name: beamform
       params:
         beamformer: delay_and_sum
-        num_patches: 100
     - name: envelope_detect
     - name: normalize
     - name: log_compress

resolvestroke/saddle/pipeline.yaml

@@ -15,7 +15,6 @@ pipeline:
     - name: beamform
       params:
         beamformer: delay_and_sum
-        num_patches: 100
     - name: envelope_detect
     - name: normalize
     - name: log_compress

siemens-healthineers/pipeline.yaml

@@ -1,8 +1,6 @@
 pipeline:
     operations:
-    -   name: beamform
-        params:
-            num_patches: 200
+    - beamform
     - envelope_detect
     -   name: normalize
         params:

stanford-murine/pipeline_hadamard.yaml

@@ -11,7 +11,6 @@ pipeline:
         params:
             enable_pfield: false
             # Bound work units so the deep C5-2v grid fits on a 24 GB GPU.
-            num_patches: 1000
     - envelope_detect
     # Use the true maximum; percentile normalization would clip strong reflectors.
     -   name: normalize

stanford-murine/pipeline_multifocal.yaml

@@ -11,7 +11,6 @@ pipeline:
         params:
             enable_pfield: false
             # Bound work units so the deep C5-2v grid fits on a 24 GB GPU.
-            num_patches: 1000
     - envelope_detect
     # Use the true maximum; percentile normalization would clip strong reflectors.
     -   name: normalize

stanford-murine/pipeline_synthetic_aperture.yaml

@@ -12,7 +12,6 @@ pipeline:
         params:
             enable_pfield: false
             # Bound work units so the deep C5-2v grid fits on a 24 GB GPU.
-            num_patches: 1000
     - envelope_detect
     # Use the true maximum; percentile normalization would clip strong reflectors.
     -   name: normalize

technion/bladder/pipeline.yaml

@@ -25,7 +25,6 @@ pipeline:
     - name: beamform
       params:
         beamformer: delay_and_sum
-        num_patches: 90
         enable_aligned_apodization: true   # scanline one-hot transmit mask
     - name: envelope_detect
     - name: normalize

technion/cardiac/pipeline.yaml

@@ -29,7 +29,6 @@ pipeline:
     - name: beamform
       params:
         beamformer: delay_and_sum
-        num_patches: 70
         enable_aligned_apodization: true
     - name: envelope_detect
     - name: normalize

technion/phantom/pipeline.yaml

@@ -25,7 +25,6 @@ pipeline:
     - name: beamform
       params:
         beamformer: delay_and_sum
-        num_patches: 90
         enable_aligned_apodization: true   # scanline one-hot transmit mask
     - name: envelope_detect
     - name: normalize

tel-aviv/phantom/pipeline.yaml

@@ -14,7 +14,6 @@ pipeline:
       params:
         beamformer: delay_and_sum
         enable_pfield: false
-        num_patches: 100
     - name: envelope_detect
     - name: normalize
     - name: log_compress

tue-aaa/pipeline.yaml

@@ -8,9 +8,7 @@ pipeline:
             size: 64
             end: 96
     - demodulate
-    -   name: beamform
-        params:
-            num_patches: 200
+    - beamform
     - envelope_detect
     -   name: normalize
         params:

tue-cardiac/pipelines/pipeline.yaml

@@ -12,12 +12,19 @@ parameters:
   zlims: [0.0, 0.11354112]
 pipeline:
   operations:
-    - {name: cast, params: {dtype: float32}}
+    - name: cast
+      params:
+        dtype: float32
     - band_pass_filter
     - apply_window
     - demodulate
-    - {name: downsample, params: {factor: 2}}
-    - {name: beamform, params: {beamformer: delay_and_sum, enable_pfield: true, num_patches: 100}}
+    - name: downsample
+      params:
+        factor: 2
+    - name: beamform
+      params:
+        beamformer: delay_and_sum
+        enable_pfield: true
     - envelope_detect
     - normalize
     - log_compress

tue-cardiac/pipelines/pipeline_hadamard.yaml

@@ -12,13 +12,24 @@ parameters:
   zlims: [0.0, 0.11354112]
 pipeline:
   operations:
-    - {name: cast, params: {dtype: float32}}
+    - name: cast
+      params:
+        dtype: float32
     - band_pass_filter
     - apply_window
-    - {name: refocus, params: {method: adjoint, param: 0, jit_compile: false}}
+    - name: refocus
+      params:
+        method: adjoint
+        param: 0
+        jit_compile: false
     - demodulate
-    - {name: downsample, params: {factor: 2}}
-    - {name: beamform, params: {beamformer: delay_and_sum, enable_pfield: false, num_patches: 100}}
+    - name: downsample
+      params:
+        factor: 2
+    - name: beamform
+      params:
+        beamformer: delay_and_sum
+        enable_pfield: false
     - envelope_detect
     - normalize
     - log_compress

tue-cardiac/pipelines/pipeline_harmonic.yaml

@@ -12,12 +12,19 @@ parameters:
   zlims: [0.0, 0.11354112]
 pipeline:
   operations:
-    - {name: cast, params: {dtype: float32}}
+    - name: cast
+      params:
+        dtype: float32
     - band_pass_filter
     - apply_window
     - demodulate
-    - {name: downsample, params: {factor: 2}}
-    - {name: beamform, params: {beamformer: delay_and_sum, enable_pfield: true, num_patches: 100}}
+    - name: downsample
+      params:
+        factor: 2
+    - name: beamform
+      params:
+        beamformer: delay_and_sum
+        enable_pfield: true
     - envelope_detect
     - normalize
     - log_compress

tue-cardiac/pipelines/pipeline_random.yaml

@@ -12,13 +12,24 @@ parameters:
   zlims: [0.0, 0.11354112]
 pipeline:
   operations:
-    - {name: cast, params: {dtype: float32}}
+    - name: cast
+      params:
+        dtype: float32
     - band_pass_filter
     - apply_window
-    - {name: refocus, params: {method: tikhonov, param: 0.01, jit_compile: false}}
+    - name: refocus
+      params:
+        method: tikhonov
+        param: 0.01
+        jit_compile: false
     - demodulate
-    - {name: downsample, params: {factor: 2}}
-    - {name: beamform, params: {beamformer: delay_and_sum, enable_pfield: false, num_patches: 100}}
+    - name: downsample
+      params:
+        factor: 2
+    - name: beamform
+      params:
+        beamformer: delay_and_sum
+        enable_pfield: false
     - envelope_detect
     - normalize
     - log_compress

tue-cardiac/reconstruct.py

@@ -1,104 +0,0 @@
-#!/usr/bin/env python3
-"""Reconstruct one track/frame from a TU/e Cardiac multi-track zea file."""
-
-from __future__ import annotations
-
-import os
-
-os.environ.setdefault("KERAS_BACKEND", "jax")
-os.environ.setdefault("MPLBACKEND", "Agg")
-
-import argparse
-from pathlib import Path
-
-import matplotlib.pyplot as plt
-import numpy as np
-import zea
-from zea import Config, File, Pipeline
-
-
-HERE = Path(__file__).resolve().parent
-PIPELINE_FOR_TRACK = {
-    "focused_fund": "pipeline.yaml",
-    "focused_harm": "pipeline_harmonic.yaml",
-    "wide_fund": "pipeline.yaml",
-    "wide_harm": "pipeline_harmonic.yaml",
-    "planewave": "pipeline.yaml",
-    "diverging": "pipeline.yaml",
-    "hadamard": "pipeline_hadamard.yaml",
-    "random": "pipeline_random.yaml",
-}
-TRACKS = tuple(PIPELINE_FOR_TRACK)
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("zea_file", type=Path)
-    parser.add_argument("--track", required=True, choices=TRACKS)
-    parser.add_argument("--frame", type=int, default=0)
-    parser.add_argument("--pipeline", type=Path, default=None)
-    parser.add_argument("--output", type=Path, default=None)
-    parser.add_argument("--device", default=None, help="e.g. cpu, cuda:0, auto:1")
-    args = parser.parse_args()
-
-    pipeline_path = args.pipeline or HERE / "pipelines" / PIPELINE_FOR_TRACK[args.track]
-    output_path = args.output or HERE / "reference" / (
-        f"{args.zea_file.stem}_{args.track}_frame-{args.frame:03d}.png"
-    )
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-
-    zea.init_device(device=args.device, verbose=False)
-    config = Config.from_path(str(pipeline_path))
-    pipeline = Pipeline.from_config(config)
-
-    with File(str(args.zea_file)) as handle:
-        labels = list(handle.track_labels)
-        if args.track not in labels:
-            raise ValueError(f"track {args.track!r} not found; available tracks: {labels}")
-        track = handle.tracks[labels.index(args.track)]
-        n_frames = int(track.data.raw_data.shape[0])
-        if not 0 <= args.frame < n_frames:
-            raise ValueError(f"frame {args.frame} outside 0..{n_frames - 1}")
-        parameters = track.load_parameters(**config.parameters)
-        raw = track.data.raw_data[args.frame : args.frame + 1]
-
-    inputs = pipeline.prepare_parameters(parameters)
-    outputs = pipeline(**{pipeline.key: raw}, **inputs)
-    image = np.asarray(outputs[pipeline.output_key])[0]
-    dynamic_range = tuple(config.parameters.dynamic_range)
-
-    # Scan conversion leaves pixels outside the polar sector undefined. Render
-    # those expected out-of-sector values at the display floor.
-    image = np.nan_to_num(
-        image,
-        nan=dynamic_range[0],
-        neginf=dynamic_range[0],
-        posinf=dynamic_range[1],
-    )
-
-    extent = getattr(parameters, "extent_imshow", None)
-    if extent is not None:
-        extent = np.asarray(extent) * 1e3
-    zea.visualize.set_mpl_style()
-    figure, axis = plt.subplots(figsize=(6, 6))
-    rendered = axis.imshow(
-        image,
-        extent=extent,
-        cmap="gray",
-        vmin=dynamic_range[0],
-        vmax=dynamic_range[1],
-        aspect="auto",
-    )
-    axis.set_xlabel("Lateral (mm)")
-    axis.set_ylabel("Depth (mm)")
-    axis.set_title(f"{args.zea_file.stem} — {args.track} — frame {args.frame}")
-    figure.colorbar(rendered, ax=axis, label="dB")
-    figure.tight_layout()
-    figure.savefig(output_path, dpi=150, bbox_inches="tight", metadata={})
-    plt.close(figure)
-    print(f"saved {output_path}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())

tue-carotid/README.md

@@ -17,6 +17,20 @@ language:
 
 # TU/e Carotid 2023
 
+![Longitudinal view of a carotid bifurcation](assets/5_long_bifur_R_0000.gif)
+
+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).
+
+`zea` renders it straight from the Hub with the
+`pipeline.yaml` in this folder. Try it out with the following command:
+
+```bash
+zea process \
+  --dataset hf://nvidia/OpenH-RF/tue-carotid/data/5_long_bifur_R_0000.hdf5 \
+  --config hf://nvidia/OpenH-RF/tue-carotid/pipeline.yaml
+```
+
 ## Dataset Description
 
 The dataset includes carotid artery scans from 10 subjects.

tue-carotid/pipeline.yaml

@@ -17,7 +17,6 @@ pipeline:
       params:
         beamformer: delay_and_sum
         enable_pfield: true
-        num_patches: 1024
     - envelope_detect
     - normalize
     - log_compress

tumunich/pipeline.yaml

@@ -21,8 +21,7 @@ pipeline:
                 params:
                     jit_compile: false
                     with_batch_dim: false
-            params:
-                num_patches: 200
+            params: {}
         - reshape_grid
     - envelope_detect
     -   name: normalize
@@ -30,7 +29,7 @@ pipeline:
             output_range:
             - 0.0
             - 1.0
-    - power_compress
+    - log_compress
 parameters:
     f_number: 1.155
     apply_lens_correction: true

twente-cavitation/README.md

@@ -1,117 +1,117 @@
----
-pretty_name: "OpenH-RF —  Hermen de Roo / Passive cavitation detection"
-license: cc-by-4.0
-task_categories:
-  - image-classification
-tags:
-  - ultrasound
-  - rf                   
-  - openh-rf
-  - cavitation
-language:
-  - en
-size_categories:
-  - 1K<n<10K                 
----
-
-
-## Dataset Description
-The collected data is for cavitation mapping of microbubbles, insonified with focused ultrasound at various pressures and flowrates. This data applicable to therapeutic ultrasound and local drug delivery in any part of the human body. The used sensor hardware is a Verasonics research system with an L11-4v transducer for recording the bubble response during the treatment. Insonification is done using a single element transducer at 2.25MHz. The insonification is done with a 1000 cycles long pulse at 2.25MHz, where the first and last 2 microseconds are used for ramping up and down the pressure. The pulse repetition frequency used is 20Hz, repeated 400 times.
-
-
-## Dataset Contributor(s)
-Hermen de Roo
-Michel Versluis
-Guillaume Lajoinie (contact email: g.p.r.lajoinie@utwente.nl)
-
-
-## Dataset Creation Date
-Data recorded on 01/19/2026. Dataset created on 07/09/2026.
-
-## License / Terms of Use
-I confirm that the data is cleared for use under CC BY 4.0.
-
-## Intended Usage
-The dataset contains data over a large pressure range, from very low pressures up to the very high pressures used in therapeutic ultrasound. With this data one can quantify the treatment threshold and treatment effects over this wide range. The dataset also includes data for different levels of perfusion by varying the flowrate, from which the effect of perfusion on treatment efficacy can be studied. The data is intended to be processed with passive cavitation detection algorithms.
-
-## Dataset Characterization
-- **Data Collection Method:** phantom
-- **Labeling Method:** N/A 
-- **Acquisition system:** Verasonics Vantage 256, L11-4v transducer. 128 elements, 7.24MHz center frequency, 27.778 MHz sampling rate
-
-## Dataset Format
-.zea file format. No preprocessing is applied. 
-
-## Dataset Quantification
-
-**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.
-- **Stored HDF5 size:** 10.85 GB (10,850,533,376 bytes).
-- All recordings were taken under identical conditions, except for the driving pressure and flowrate of the microbubble solution through the channel.
-
-Each acquisition is one zea HDF5 file with a single track (`tracks/track_0`). The
-per-frame channel data plus the scan/probe fields needed to reconstruct it are:
-
-| Field | Shape | dtype | Units | Description |
-|---|---|---|---|---|
-| `data/raw_data` | (400, 1, 16384, 128, 1) | int16 | a.u. (ADC counts) | Receive RF channel data: 400 frames × 1 transmit event × 16384 axial samples × 128 elements × 1 channel. This is a passive acquisition — the array only receives. |
-| `probe/probe_geometry` | (128, 3) | float32 | m | (x, y, z) position of each of the 128 elements (L11-4v, 0.3 mm pitch). |
-| `probe/probe_center_frequency` | scalar | float32 | Hz | Probe center frequency (7.24 MHz). |
-| `probe/element_width` | scalar | float32 | m | Element width (0.27 mm). |
-| `scan/sampling_frequency` | scalar | float32 | Hz | RF sampling rate (27.78 MHz). |
-| `scan/center_frequency` | scalar | float32 | Hz | Receive center frequency (≈7.35 MHz). |
-| `scan/demodulation_frequency` | scalar | float32 | Hz | Demodulation frequency used for IQ conversion (≈6.94 MHz). |
-| `scan/sound_speed` | scalar | float32 | m/s | Assumed speed of sound (1480). |
-| `scan/initial_times` | (1,) | float32 | s | Time of the first recorded sample relative to transmit (0). |
-| `scan/t0_delays` | (1, 128) | float32 | s | Per-element transmit delays (all 0 — array does not transmit). |
-| `scan/tx_apodizations` | (1, 128) | float32 | a.u. | Transmit apodization per element (all 0 — passive acquisition; `reconstruct.py` overrides to ones for receive beamforming). |
-| `scan/time_to_next_transmit` | (400, 1) | float32 | s | Interval to the next transmit per frame (PRF = 20 Hz). |
-| `scan/tgc_gain_curve` | (16384,) | float32 | a.u. | Time-gain-compensation curve applied along the axial dimension. |
-| `tracks/track_0/transmit_only` | scalar | bool | — | False (the array receives). |
-
-> **Note.** The table below is the **acquisition matrix** — it lists which files exist
-> and under what driving pressure / flowrate, not the internal layout of a sample.
-
-Files are named `cavitation_bubbles_<pressure>kPa_<flowrate>mL.hdf5`, where
-`<flowrate>` is the microbubble flowrate in mL/min (`01` = 0.1, `05` = 0.5, `2` = 2).
-
-| Name | Acoustic driving pressure [kPa]| Microbubble flowrate [mL/min] |
-|---                                    |---   |---  |
-| cavitation_bubbles_10kPa_01mL.hdf5    | 10   | 0.1 |
-| cavitation_bubbles_25kPa_01mL.hdf5    | 25   | 0.1 |
-| cavitation_bubbles_50kPa_01mL.hdf5    | 50   | 0.1 |
-| cavitation_bubbles_75kPa_01mL.hdf5    | 75   | 0.1 |
-| cavitation_bubbles_100kPa_01mL.hdf5   | 100  | 0.1 |
-| cavitation_bubbles_250kPa_01mL.hdf5   | 250  | 0.1 |
-| cavitation_bubbles_500kPa_01mL.hdf5   | 500  | 0.1 |
-| cavitation_bubbles_750kPa_01mL.hdf5   | 750  | 0.1 |
-| cavitation_bubbles_1000kPa_01mL.hdf5  | 1000 | 0.1 |
-| cavitation_bubbles_10kPa_05mL.hdf5    | 10   | 0.5 |
-| cavitation_bubbles_50kPa_05mL.hdf5    | 50   | 0.5 |
-| cavitation_bubbles_100kPa_05mL.hdf5   | 100  | 0.5 |
-| cavitation_bubbles_500kPa_05mL.hdf5   | 500  | 0.5 |
-| cavitation_bubbles_1000kPa_05mL.hdf5  | 1000 | 0.5 |
-| cavitation_bubbles_10kPa_2mL.hdf5     | 10   | 2   |
-| cavitation_bubbles_50kPa_2mL.hdf5     | 50   | 2   |
-| cavitation_bubbles_100kPa_2mL.hdf5    | 100  | 2   |
-| cavitation_bubbles_500kPa_2mL.hdf5    | 500  | 2   |
-| cavitation_bubbles_1000kPa_2mL.hdf5   | 1000 | 2   |
-
-
-## Subject Metadata
-Only one phantom was used. This is a phantom made of PVCp with a single flow channel ~200 micrometer diameter. The used scanner is a Verasonics Vantage 256 with a L11-4v transducer.
-
-## Data Validation
-An reconstruction pipeline can be found in pipeline.yaml. The script reconstruct.py is an example of the reconstruction of the data, using the minimum variance / Capon beamformer. An example reconstruction is saved with this dataset, and named reference_image_1000kPa_2mL_per_min.png, which was generated using the Capon beamforming algorithm using epsilon = 2, on the datafile named cavitation_bubbles_1000kPa_2mL_per_min.hdf5. By default the script saves the map next to the input file with the same name and a `.png` extension (e.g. `my_file.hdf5` → `my_file.png`); pass `--output` to override. Usage:
-    python reconstruct.py
-    python reconstruct.py --input my_file.hdf5 --device cpu
-    python reconstruct.py --input my_file.hdf5 --output my_map.png --frames 20 --device cuda:0
-
-
-## Known Issues
-No known issues.
-
-## Ethical Considerations
-This is phantom acquisition data, hence no human-subject IRB/HIPAA approval is required.
+---
+pretty_name: "OpenH-RF —  Hermen de Roo / Passive cavitation detection"
+license: cc-by-4.0
+task_categories:
+  - image-classification
+tags:
+  - ultrasound
+  - rf                   
+  - openh-rf
+  - cavitation
+language:
+  - en
+size_categories:
+  - 1K<n<10K                 
+---
+
+
+## Dataset Description
+The collected data is for cavitation mapping of microbubbles, insonified with focused ultrasound at various pressures and flowrates. This data applicable to therapeutic ultrasound and local drug delivery in any part of the human body. The used sensor hardware is a Verasonics research system with an L11-4v transducer for recording the bubble response during the treatment. Insonification is done using a single element transducer at 2.25MHz. The insonification is done with a 1000 cycles long pulse at 2.25MHz, where the first and last 2 microseconds are used for ramping up and down the pressure. The pulse repetition frequency used is 20Hz, repeated 400 times.
+
+
+## Dataset Contributor(s)
+Hermen de Roo
+Michel Versluis
+Guillaume Lajoinie (contact email: g.p.r.lajoinie@utwente.nl)
+
+
+## Dataset Creation Date
+Data recorded on 01/19/2026. Dataset created on 07/09/2026.
+
+## License / Terms of Use
+I confirm that the data is cleared for use under CC BY 4.0.
+
+## Intended Usage
+The dataset contains data over a large pressure range, from very low pressures up to the very high pressures used in therapeutic ultrasound. With this data one can quantify the treatment threshold and treatment effects over this wide range. The dataset also includes data for different levels of perfusion by varying the flowrate, from which the effect of perfusion on treatment efficacy can be studied. The data is intended to be processed with passive cavitation detection algorithms.
+
+## Dataset Characterization
+- **Data Collection Method:** phantom
+- **Labeling Method:** N/A 
+- **Acquisition system:** Verasonics Vantage 256, L11-4v transducer. 128 elements, 7.24MHz center frequency, 27.778 MHz sampling rate
+
+## Dataset Format
+.zea file format. No preprocessing is applied. 
+
+## Dataset Quantification
+
+**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.
+- **Stored HDF5 size:** 10.85 GB (10,850,533,376 bytes).
+- All recordings were taken under identical conditions, except for the driving pressure and flowrate of the microbubble solution through the channel.
+
+Each acquisition is one zea HDF5 file with a single track (`tracks/track_0`). The
+per-frame channel data plus the scan/probe fields needed to reconstruct it are:
+
+| Field | Shape | dtype | Units | Description |
+|---|---|---|---|---|
+| `data/raw_data` | (400, 1, 16384, 128, 1) | int16 | a.u. (ADC counts) | Receive RF channel data: 400 frames × 1 transmit event × 16384 axial samples × 128 elements × 1 channel. This is a passive acquisition — the array only receives. |
+| `probe/probe_geometry` | (128, 3) | float32 | m | (x, y, z) position of each of the 128 elements (L11-4v, 0.3 mm pitch). |
+| `probe/probe_center_frequency` | scalar | float32 | Hz | Probe center frequency (7.24 MHz). |
+| `probe/element_width` | scalar | float32 | m | Element width (0.27 mm). |
+| `scan/sampling_frequency` | scalar | float32 | Hz | RF sampling rate (27.78 MHz). |
+| `scan/center_frequency` | scalar | float32 | Hz | Receive center frequency (≈7.35 MHz). |
+| `scan/demodulation_frequency` | scalar | float32 | Hz | Demodulation frequency used for IQ conversion (≈6.94 MHz). |
+| `scan/sound_speed` | scalar | float32 | m/s | Assumed speed of sound (1480). |
+| `scan/initial_times` | (1,) | float32 | s | Time of the first recorded sample relative to transmit (0). |
+| `scan/t0_delays` | (1, 128) | float32 | s | Per-element transmit delays (all 0 — array does not transmit). |
+| `scan/tx_apodizations` | (1, 128) | float32 | a.u. | Transmit apodization per element (all 0 — passive acquisition; `reconstruct.py` overrides to ones for receive beamforming). |
+| `scan/time_to_next_transmit` | (400, 1) | float32 | s | Interval to the next transmit per frame (PRF = 20 Hz). |
+| `scan/tgc_gain_curve` | (16384,) | float32 | a.u. | Time-gain-compensation curve applied along the axial dimension. |
+| `tracks/track_0/transmit_only` | scalar | bool | — | False (the array receives). |
+
+> **Note.** The table below is the **acquisition matrix** — it lists which files exist
+> and under what driving pressure / flowrate, not the internal layout of a sample.
+
+Files are named `cavitation_bubbles_<pressure>kPa_<flowrate>mL.hdf5`, where
+`<flowrate>` is the microbubble flowrate in mL/min (`01` = 0.1, `05` = 0.5, `2` = 2).
+
+| Name | Acoustic driving pressure [kPa]| Microbubble flowrate [mL/min] |
+|---                                    |---   |---  |
+| cavitation_bubbles_10kPa_01mL.hdf5    | 10   | 0.1 |
+| cavitation_bubbles_25kPa_01mL.hdf5    | 25   | 0.1 |
+| cavitation_bubbles_50kPa_01mL.hdf5    | 50   | 0.1 |
+| cavitation_bubbles_75kPa_01mL.hdf5    | 75   | 0.1 |
+| cavitation_bubbles_100kPa_01mL.hdf5   | 100  | 0.1 |
+| cavitation_bubbles_250kPa_01mL.hdf5   | 250  | 0.1 |
+| cavitation_bubbles_500kPa_01mL.hdf5   | 500  | 0.1 |
+| cavitation_bubbles_750kPa_01mL.hdf5   | 750  | 0.1 |
+| cavitation_bubbles_1000kPa_01mL.hdf5  | 1000 | 0.1 |
+| cavitation_bubbles_10kPa_05mL.hdf5    | 10   | 0.5 |
+| cavitation_bubbles_50kPa_05mL.hdf5    | 50   | 0.5 |
+| cavitation_bubbles_100kPa_05mL.hdf5   | 100  | 0.5 |
+| cavitation_bubbles_500kPa_05mL.hdf5   | 500  | 0.5 |
+| cavitation_bubbles_1000kPa_05mL.hdf5  | 1000 | 0.5 |
+| cavitation_bubbles_10kPa_2mL.hdf5     | 10   | 2   |
+| cavitation_bubbles_50kPa_2mL.hdf5     | 50   | 2   |
+| cavitation_bubbles_100kPa_2mL.hdf5    | 100  | 2   |
+| cavitation_bubbles_500kPa_2mL.hdf5    | 500  | 2   |
+| cavitation_bubbles_1000kPa_2mL.hdf5   | 1000 | 2   |
+
+
+## Subject Metadata
+Only one phantom was used. This is a phantom made of PVCp with a single flow channel ~200 micrometer diameter. The used scanner is a Verasonics Vantage 256 with a L11-4v transducer.
+
+## Data Validation
+An reconstruction pipeline can be found in pipeline.yaml. The script reconstruct.py is an example of the reconstruction of the data, using the minimum variance / Capon beamformer. An example reconstruction is saved with this dataset, and named reference_image_1000kPa_2mL_per_min.png, which was generated using the Capon beamforming algorithm using epsilon = 2, on the datafile named cavitation_bubbles_1000kPa_2mL_per_min.hdf5. By default the script saves the map next to the input file with the same name and a `.png` extension (e.g. `my_file.hdf5` → `my_file.png`); pass `--output` to override. Usage:
+    python reconstruct.py
+    python reconstruct.py --input my_file.hdf5 --device cpu
+    python reconstruct.py --input my_file.hdf5 --output my_map.png --frames 20 --device cuda:0
+
+
+## Known Issues
+No known issues.
+
+## Ethical Considerations
+This is phantom acquisition data, hence no human-subject IRB/HIPAA approval is required.

twente-cavitation/pipeline.yaml

@@ -1,20 +1,22 @@
-pipeline:
-    operations:
-    -   name: keras.ops.cast
-        params:
-            dtype: float32
-    - demodulate
-    -   name: beamform
-        params:
-            num_patches: 8
-    - envelope_detect
-parameters:
-    grid_size_x: 387
-    grid_size_z: 577
-    xlims:
-    - -0.019
-    - 0.019
-    zlims:
-    - 0.002
-    - 0.06
-    apply_lens_correction: true
+pipeline:
+    operations:
+    -   name: keras.ops.cast
+        params:
+            dtype: float32
+    - demodulate
+    -   name: beamform
+        params:
+            beamformer: minimum_variance
+            subarray_size: 32
+            diagonal_loading: 0.01
+    - envelope_detect
+parameters:
+    grid_size_x: 387
+    grid_size_z: 577
+    xlims:
+    - -0.019
+    - 0.019
+    zlims:
+    - 0.002
+    - 0.06
+    apply_lens_correction: true

twente-microbubblesim/README.md

@@ -57,7 +57,7 @@ deep-learning methods for microbubble super-resolution imaging.
 ## Dataset Characterization
 
 - **Data collection method:** Synthetic.
-- **Ground truth:** Simulated 3-D microbubble positions are stored as Zea custom
+- **Ground truth:** Simulated 3-D microbubble positions are stored as zea custom
   elements (`bubble_x`, `bubble_y`, and `bubble_z`).
 - **Bubble populations:** Monodisperse bubbles with a radius of 2.4 micrometres
   and 5% standard deviation, and a polydisperse SonoVue population.
@@ -79,7 +79,7 @@ deep-learning methods for microbubble super-resolution imaging.
 
 ## Dataset Format
 
-Each bubble distribution is stored in one Zea HDF5 file containing 12 pulse
+Each bubble distribution is stored in one zea HDF5 file containing 12 pulse
 tracks. The raw channel data in every `tracks/track_i/data/raw_data` dataset
 have shape `(1, 1, 8446, 96, 1)`: one frame, one transmit, 8,446 time samples,
 96 receive elements, and one real RF channel. RF values are `float32`; their
@@ -212,7 +212,7 @@ to one `.hdf5` acquisition file.
 
 ## Data Validation
 
-All 500 HDF5 files were checked for the expected Zea container structure, 12
+All 500 HDF5 files were checked for the expected zea container structure, 12
 ordered and labelled tracks, RF shape `(1, 1, 8446, 96, 1)`, and track-specific
 pulse metadata. The original submission passed zea 0.1.2 validation. Representative RF traces and pulse
 waveforms were also compared with their pulse-folder sources with no mismatch.

twente-microbubblesim/migrate_custom_names.py

@@ -1,239 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Collision-safe custom-key migration followed by a verified zea resave.
-
-Only source notation is encoded in the replacement names. In particular,
-``bubble_initial_r0`` preserves R0 separately from the source's distinct r0.
-"""
-from __future__ import annotations
-
-import argparse
-import hashlib
-import itertools
-import json
-import math
-import os
-from pathlib import Path
-import re
-import shutil
-import tempfile
-
-import hdf5plugin  # Register the source and destination compression filters.
-import h5py
-import numpy as np
-
-CUSTOM_NAME_MAP = {
-    "bubble_R0": "bubble_initial_r0",
-    "bubble_p_dB": "bubble_p_db",
-    **{f"track_{i}_pulse_A": f"track_{i}_pulse_a" for i in range(12)},
-}
-LEGACY_NAME_ATTRIBUTE = "source_custom_name"
-SNAKE_CASE = re.compile(r"^[a-z_][a-z0-9_]*$")
-
-
-def canonicalize_custom_names(custom):
-    """Return canonical keys for old or new files without merging collisions."""
-    result = dict(custom)
-    for old, new in CUSTOM_NAME_MAP.items():
-        if old in result:
-            if new in result:
-                raise ValueError(f"Both legacy {old!r} and canonical {new!r} exist")
-            result[new] = result.pop(old)
-    return result
-
-
-def sha256(path):
-    digest = hashlib.sha256()
-    with open(path, "rb") as stream:
-        for block in iter(lambda: stream.read(8*1024**2), b""):
-            digest.update(block)
-    return digest.hexdigest()
-
-
-def mapped_path(path):
-    if path.startswith("custom/"):
-        return "custom/" + CUSTOM_NAME_MAP.get(path[7:], path[7:])
-    return path
-
-
-def object_paths(file):
-    paths, seen = [], set()
-    def walk(group):
-        for key in group:
-            if not isinstance(group.get(key, getlink=True), h5py.HardLink):
-                raise ValueError("External/soft links are not supported")
-            obj = group[key]
-            address = h5py.h5o.get_info(obj.id).addr
-            if address in seen:
-                raise ValueError("Aliased objects are not supported")
-            seen.add(address)
-            path = obj.name.lstrip("/")
-            paths.append(path)
-            if isinstance(obj, h5py.Group):
-                walk(obj)
-            elif obj.external or obj.is_virtual or h5py.check_dtype(ref=obj.dtype):
-                raise ValueError("External, virtual, and reference datasets are not supported")
-            elif obj.shape is None:
-                raise ValueError("Null datasets are not supported")
-    walk(file)
-    return sorted(paths)
-
-
-def planned_renames(file):
-    if "custom" not in file or not isinstance(file["custom"], h5py.Group):
-        raise ValueError("Expected Twente custom group")
-    names = set(file["custom"])
-    if "bubble_r0" not in names or not names.intersection({"bubble_R0", "bubble_initial_r0"}):
-        raise ValueError("Expected distinct source R0 and r0 fields")
-    plan = {}
-    for name in names:
-        target = CUSTOM_NAME_MAP.get(name, name)
-        if not SNAKE_CASE.fullmatch(target):
-            raise ValueError(f"Unrecognized non-snake-case custom key: {name!r}")
-        if target != name:
-            if target in names:
-                raise ValueError(f"Rename collision: {name!r} -> {target!r}")
-            plan["custom/" + name] = "custom/" + target
-    for path in object_paths(file):
-        if path.startswith("custom/"):
-            for segment in mapped_path(path).split("/"):
-                if not SNAKE_CASE.fullmatch(segment):
-                    raise ValueError(f"Unrecognized non-snake-case custom path: {path!r}")
-        obj = file[path]
-        for key in obj.attrs:
-            if h5py.check_dtype(ref=obj.attrs.get_id(key).dtype):
-                raise ValueError("Reference attributes are not supported")
-    return plan
-
-
-def equal(a, b):
-    a, b = np.asarray(a), np.asarray(b)
-    if a.shape != b.shape:
-        return False
-    if a.dtype.kind in "fc" and b.dtype.kind in "fc":
-        return np.array_equal(a, b, equal_nan=True)
-    return np.array_equal(a, b)
-
-
-def blocks(shape, itemsize, budget=16*1024**2):
-    if not shape:
-        yield ()
-        return
-    if not all(shape):
-        return
-    tile = list(shape)
-    while math.prod(tile)*max(itemsize, 8) > budget:
-        axis = max(range(len(tile)), key=tile.__getitem__)
-        tile[axis] = (tile[axis]+1)//2
-    for start in itertools.product(*(range(0, n, t) for n, t in zip(shape, tile))):
-        yield tuple(slice(s, min(s+t, n)) for s, t, n in zip(start, tile, shape))
-
-
-def validate_migration(source, output):
-    """Check every dataset value and attribute, allowing only declared renames."""
-    with h5py.File(source, "r") as a, h5py.File(output, "r") as b:
-        plan = planned_renames(a)
-        left, right = object_paths(a), object_paths(b)
-        if sorted(mapped_path(p) for p in left) != right:
-            raise ValueError("Object inventory changed beyond the explicit name mapping")
-        if str(b.attrs.get("zea_version")) != "0.1.6":
-            raise ValueError("Output must be written by zea 0.1.6")
-        datasets = 0
-        for path in ["/"] + left:
-            x, y = a[path], b[mapped_path(path)]
-            if type(x) is not type(y):
-                raise ValueError(f"Object type changed: {path}")
-            if isinstance(x, h5py.Dataset):
-                if x.shape != y.shape or x.dtype != y.dtype:
-                    raise ValueError(f"Shape or dtype changed: {path}")
-                for block in blocks(x.shape, x.dtype.itemsize):
-                    old, new = np.asarray(x[block]), np.asarray(y[block])
-                    same = equal(old, new) if old.dtype.hasobject else old.tobytes() == new.tobytes()
-                    if not same:
-                        raise ValueError(f"Dataset values changed: {path}")
-                datasets += 1
-            expected = set(x.attrs)
-            if path in plan:
-                expected.add(LEGACY_NAME_ATTRIBUTE)
-                if y.attrs.get(LEGACY_NAME_ATTRIBUTE) != path.split("/")[-1]:
-                    raise ValueError(f"Missing original-name provenance: {path}")
-            if set(y.attrs) != expected:
-                raise ValueError(f"Attribute inventory changed: {path}")
-            for key in x.attrs:
-                if path == "/" and key == "zea_version":
-                    continue
-                if not equal(x.attrs[key], y.attrs[key]):
-                    raise ValueError(f"Attribute changed: {path}@{key}")
-        return dict(differences=[], all_dataset_values_checked=True, datasets_checked=datasets,
-                    allowed_changes=[dict(path=p, new_path=q, category="user_approved_custom_rename")
-                                     for p, q in sorted(plan.items())])
-
-
-def restore_attributes(source, output, plan):
-    # zea regenerates standard attributes; retain the original scientific metadata.
-    with h5py.File(source, "r") as a, h5py.File(output, "r+") as b:
-        paths = object_paths(a)
-        if sorted(mapped_path(p) for p in paths) != object_paths(b):
-            raise ValueError("Resave changed the object inventory")
-        for path in ["/"] + paths:
-            x, y = a[path], b[mapped_path(path)]
-            for key in list(y.attrs):
-                if not (path == "/" and key == "zea_version"):
-                    del y.attrs[key]
-            for key in x.attrs:
-                if path == "/" and key == "zea_version":
-                    continue
-                aid = x.attrs.get_id(key)
-                if h5py.check_dtype(ref=aid.dtype):
-                    raise ValueError("Reference attributes are not supported")
-                y.attrs.create(key, x.attrs[key], dtype=aid.dtype, shape=aid.shape)
-            if path in plan:
-                if LEGACY_NAME_ATTRIBUTE in y.attrs:
-                    if y.attrs[LEGACY_NAME_ATTRIBUTE] != path.split("/")[-1]:
-                        raise ValueError("Conflicting original-name provenance")
-                else:
-                    y.attrs[LEGACY_NAME_ATTRIBUTE] = path.split("/")[-1]
-
-
-def resave_file(source, output, *, _writer=None):
-    """Copy/rename/resave/verify one full file; never modify the source."""
-    source, output = Path(source), Path(output)
-    if source.resolve() == output.resolve() or output.exists():
-        raise ValueError("Output must be a new path distinct from the source")
-    with h5py.File(source, "r") as f:
-        plan = planned_renames(f)
-    source_sha = sha256(source)
-    if _writer is None:
-        from importlib.metadata import version
-        if version("zea") != "0.1.6":
-            raise RuntimeError("This migration requires zea 0.1.6")
-        from zea.data.file_operations import resave
-        _writer = resave
-    output.parent.mkdir(parents=True, exist_ok=True)
-    with tempfile.TemporaryDirectory(prefix=".twente-resave-", dir=output.parent) as directory:
-        renamed = Path(directory) / "renamed-input.hdf5"
-        candidate = Path(directory) / "candidate.hdf5"
-        shutil.copyfile(source, renamed)
-        with h5py.File(renamed, "r+") as f:
-            for old, new in plan.items():
-                f.move(old, new)
-        _writer(renamed, candidate)
-        restore_attributes(source, candidate, plan)
-        result = validate_migration(source, candidate)
-        if sha256(source) != source_sha:
-            raise ValueError("Source changed during migration")
-        # A hard link publishes the verified file without overwriting a raced output.
-        os.link(candidate, output)
-    return result
-
-
-def main():
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("input", type=Path)
-    parser.add_argument("output", type=Path)
-    args = parser.parse_args()
-    os.environ.update(CUDA_VISIBLE_DEVICES="", KERAS_BACKEND="numpy", JAX_PLATFORMS="cpu")
-    print(json.dumps(resave_file(args.input, args.output), indent=2))
-
-
-if __name__ == "__main__":
-    main()

twente-microbubblesim/utils.py

@@ -1,247 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Reconstruction loading, coordinate, plotting, and ground-truth helpers."""
-
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-
-
-def custom_map(file: Any, track_index: int = 0) -> dict[str, np.ndarray]:
-    """Return canonical custom names and resolve track-specific pulse metadata.
-
-    Both legacy and migrated files are supported. R0 and r0 remain distinct;
-    ambiguous legacy/canonical duplicates are rejected rather than overwritten.
-    """
-
-    from migrate_custom_names import canonicalize_custom_names
-
-    custom = canonicalize_custom_names(
-        {element.name: np.asarray(element.data) for element in file.custom}
-    )
-    prefix = f"track_{track_index}_"
-    for key, value in tuple(custom.items()):
-        if key.startswith(prefix):
-            custom[key.removeprefix(prefix)] = value
-    return custom
-
-
-def t_peak_override(custom: dict[str, np.ndarray]) -> np.ndarray:
-    """Return the selected track's finite beamforming peak-time override."""
-
-    if "t_peak" not in custom:
-        raise KeyError("HDF5 file is missing required custom field t_peak")
-    t_peak = float(np.asarray(custom["t_peak"]).reshape(-1)[0])
-    if not np.isfinite(t_peak):
-        raise ValueError("HDF5 custom field t_peak must be finite")
-    return np.asarray([t_peak], dtype=np.float32)
-
-
-def load_hdf5(path: str | Path,
-              num_frames: int = 1,
-              track_index: int = 0,
-              ) -> tuple[Any, Any, Any, dict[str, np.ndarray]]:
-    """Load one Zea acquisition and its custom elements."""
-
-    import zea
-
-    file = zea.File(str(path))
-    if track_index < 0 or track_index >= len(file.tracks):
-        file.close()
-        raise IndexError(
-            f"track_index {track_index} is outside [0, {len(file.tracks) - 1}]"
-        )
-    track = file.tracks[track_index]
-    parameters = track.load_parameters()
-    data = track.data.raw_data[:num_frames, parameters.selected_transmits, ...]
-    return file, parameters, data, custom_map(file, track_index=track_index)
-
-
-
-def run_bmode(
-    path: str | Path,
-    config_path: str | Path,
-    num_frames: int = 1,
-    track_index: int = 0,
-    dynamic_range: tuple[float, float] = (-30.0, 0.0),
-    xlims_cm: tuple[float, float] | None = (-1.5, 1.5),
-) -> tuple[Any, Any, np.ndarray, dict[str, np.ndarray]]:
-    """Run the configured Zea pipeline and return the image plus acquisition data.
-
-    ``xlims_cm`` controls the lateral field of view used by the DAS
-    beamformer. Zea expects these limits in metres, while this public helper
-    uses centimetres to match the plotted image axes.
-    """
-
-    import keras
-    import zea
-
-    file, parameters, data, custom = load_hdf5(
-        path, num_frames=num_frames, track_index=track_index
-    )
-    config = zea.Config.from_path(str(config_path))
-    parameters.dynamic_range = tuple(dynamic_range)
-    if xlims_cm is not None:
-        x_min_cm, x_max_cm = xlims_cm
-        if not x_min_cm < x_max_cm:
-            raise ValueError("xlims_cm must be ordered as (minimum, maximum)")
-        parameters.update(xlims=(x_min_cm * 1e-2, x_max_cm * 1e-2))
-    pipeline = zea.Pipeline.from_config(config)
-    inputs = pipeline.prepare_parameters(
-        parameters, t_peak=t_peak_override(custom)
-    )
-    inputs = {pipeline.key: data, **inputs}
-    image = keras.ops.convert_to_numpy(pipeline(**inputs)[pipeline.output_key])
-    image = np.asarray(keras.ops.squeeze(image))
-    image = np.asarray(
-        zea.display.to_8bit(
-            image,
-            dynamic_range=parameters.dynamic_range))
-    return file, parameters, image, custom
-
-
-def image_extent_cm(parameters: Any,
-                    custom: dict[str,
-                                 np.ndarray],
-                    image_shape: tuple[int,
-                                       int],
-                    xlims_cm: tuple[float, float] | None = None,
-                    ) -> tuple[float,
-                                                      float,
-                                                      float,
-                                                      float]:
-    """Calculate an x/z extent in centimeters for an image-shaped array."""
-
-    height, width = image_shape[-2:]
-    domain_width = float(np.asarray(custom.get("domain_width", np.nan)).reshape(-1)[0])
-    domain_depth = float(np.asarray(custom.get("domain_depth", np.nan)).reshape(-1)[0])
-    if not np.isfinite(domain_width):
-        geometry = np.asarray(parameters.probe_geometry)
-        domain_width = float(np.max(geometry[:, 0]) - np.min(geometry[:, 0]))
-    if not np.isfinite(domain_depth):
-        domain_depth = float(parameters.sound_speed) * \
-            image_shape[-2] / (2.0 * float(parameters.sampling_frequency))
-    if xlims_cm is None:
-        x_extent_cm = (-0.5 * domain_width * 100.0,
-                       0.5 * domain_width * 100.0)
-    else:
-        x_extent_cm = tuple(float(value) for value in xlims_cm)
-        if not x_extent_cm[0] < x_extent_cm[1]:
-            raise ValueError("xlims_cm must be ordered as (minimum, maximum)")
-    return (*x_extent_cm, 0.0, domain_depth * 100.0)
-
-
-def bubble_coordinates_cm(
-        custom: dict[str, np.ndarray]) -> tuple[np.ndarray, np.ndarray]:
-    """Return finite bubble x/z coordinates in centimeters."""
-
-    if "bubble_x" not in custom or "bubble_z" not in custom:
-        return np.empty(0, dtype=np.float32), np.empty(0, dtype=np.float32)
-    x = np.asarray(custom["bubble_x"], dtype=np.float32).reshape(-1)
-    z = np.asarray(custom["bubble_z"], dtype=np.float32).reshape(-1)
-    valid = np.isfinite(x) & np.isfinite(z)
-    return x[valid] * 100.0, z[valid] * 100.0
-
-
-def plot_bmode(
-    image: np.ndarray,
-    extent_cm: tuple[float, float, float, float],
-    output: str | Path,
-    bubble_x_cm: np.ndarray | None = None,
-    bubble_z_cm: np.ndarray | None = None,
-    show_bubbles: bool = True,
-    title: str | None = None,
-) -> None:
-    """Save a B-mode image with centimeter axes and optional GT bubbles."""
-
-    import matplotlib.pyplot as plt
-
-    image = np.asarray(image)
-    if image.ndim != 2:
-        raise ValueError(f"Expected a 2-D B-mode image, got shape {image.shape}")
-
-    x_min, x_max, z_min, z_max = extent_cm
-    fig, ax = plt.subplots(figsize=(5, 12), constrained_layout=True)
-    ax.imshow(
-        image,
-        cmap="gray",
-        origin="upper",
-        aspect="auto",
-        extent=(x_min, x_max, z_max, z_min),
-        interpolation="nearest",
-        vmin=0,
-        vmax=255,
-    )
-    if show_bubbles and bubble_x_cm is not None and bubble_z_cm is not None:
-        ax.scatter(
-            bubble_x_cm,
-            bubble_z_cm,
-            facecolors="none",
-            edgecolors="red",
-            linewidths=0.8,
-            s=28,
-            label="Bubble ground truth",
-        )
-        ax.legend(loc="upper right")
-    ax.set_xlabel("Lateral position x [cm]")
-    ax.set_ylabel("Depth z [cm]")
-    if title:
-        ax.set_title(title)
-    ax.set_xlim(x_min, x_max)
-    ax.set_ylim(z_max, z_min)
-    fig.savefig(output, dpi=150)
-    plt.close(fig)
-
-
-def plot_gt_label(
-    label: np.ndarray,
-    extent_cm: tuple[float, float, float, float],
-    output: str | Path,
-    title: str | None = None,
-) -> None:
-    """Save a ground-truth label with the same physical axes as B-mode."""
-
-    import matplotlib.pyplot as plt
-
-    x_min, x_max, z_min, z_max = extent_cm
-    fig, ax = plt.subplots(figsize=(5, 12), constrained_layout=True)
-    ax.imshow(
-        label,
-        cmap="gray",
-        origin="upper",
-        aspect="auto",
-        extent=(x_min, x_max, z_max, z_min),
-        interpolation="nearest",
-        vmin=0.0,
-        vmax=1.0,
-    )
-    ax.set_xlabel("Lateral position x [cm]")
-    ax.set_ylabel("Depth z [cm]")
-    if title:
-        ax.set_title(title)
-    ax.set_xlim(x_min, x_max)
-    ax.set_ylim(z_max, z_min)
-    fig.savefig(output, dpi=150)
-    plt.close(fig)
-
-
-def bubble_label(
-    image_shape: tuple[int, int],
-    extent_cm: tuple[float, float, float, float],
-    bubble_x_cm: np.ndarray,
-    bubble_z_cm: np.ndarray,
-) -> np.ndarray:
-    """Create a hard binary bubble label image."""
-
-    height, width = image_shape
-    x_min, x_max, z_min, z_max = extent_cm
-    label = np.zeros((height, width), dtype=np.float32)
-    x_pixels = (bubble_x_cm - x_min) / (x_max - x_min) * (width - 1)
-    z_pixels = (bubble_z_cm - z_min) / (z_max - z_min) * (height - 1)
-    x_pixels = np.rint(x_pixels).astype(int)
-    z_pixels = np.rint(z_pixels).astype(int)
-    valid = (x_pixels >= 0) & (x_pixels < width) & (z_pixels >= 0) & (z_pixels < height)
-    label[z_pixels[valid], x_pixels[valid]] = 1.0
-    return label