resolvestroke: restructure the data cards to the common layout

resolvestroke/README.md

@@ -1,5 +1,6 @@
 ---
-pretty_name: "OpenH-RF - Resolve Stroke Transcranial CEUS, Flow and Imaging Phantom Channel Data"
+name: resolvestroke
+pretty_name: "Resolve Stroke Transcranial CEUS, Flow and Imaging Phantom Channel Data"
 license: cc-by-4.0
 task_categories:
   - other
@@ -21,34 +22,46 @@ size_categories:
   - 100K<n<1M
 ---
 
-# OpenH-RF - Resolve Stroke datasets
+# Resolve Stroke Transcranial CEUS, Flow and Imaging Phantom Channel Data
 
 <a href="assets/main_image.png"><img src="assets/main_image_small.png" alt="Transcranial contrast-enhanced power Doppler of a human subject (SP03-Left, bolus +10 s), x-z maximum-intensity projection" width="360"></a>
 
-*Figure: 3D power Doppler of the transcranial CEUS acquisition `clinical/SP03-Left` at
-bolus +10 s, x-z maximum-intensity projection, reconstructed from the released channel
-data.*
-
-[Resolve Stroke](https://www.resolvestroke.com/) develops SYLVER, a
-software-driven ultrasound platform that supports clinical assessment in
-patients suspected of, or at risk for, cerebral perfusion abnormalities by
-improving visualization of cerebral vasculature and providing complementary
-information on cerebral perfusion.
-SYLVER images with a 32×32 matrix array probe at 2 MHz
-using diverging-wave transmits. CE and FDA clearances are expected in 2026.
-This directory holds the data Resolve Stroke contributes to OpenH-RF: pre-beamformed
-RF/IQ channel data from an imaging phantom, a flow phantom, and in-vivo transcranial
-acquisitions, all de-identified and released under CC BY 4.0. The in-vivo data comes
-from the CPP-approved SCULPT clinical study. Two transmit sequences are included: a
-saddle sequence that images a 2D plane for real-time B-mode, and a 4 kHz
-single-aperture volume sequence for CEUS and blood-flow measurement.
-
-Each dataset directory holds its data card (`README.md`), a `reconstruct.py` +
-`pipeline.yaml` beamforming recipe and, for the CEUS datasets, a power-Doppler
-reconstruction; the HDF5 files are on the Hub and the preview images in
-[`assets/`](assets/).
-
-## OpenH-RF Release Inventory
+*3D power Doppler of the transcranial CEUS acquisition [`clinical/SP03-Left/SP03-Left.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/resolvestroke/clinical/SP03-Left/SP03-Left.hdf5) at bolus +10 s, x-z maximum-intensity projection, reconstructed from the released channel data.*
+
+## Dataset Description
+
+[Resolve Stroke](https://www.resolvestroke.com/) develops SYLVER, a software-driven ultrasound platform that supports clinical assessment in patients suspected of, or at risk for, cerebral perfusion abnormalities by improving visualization of cerebral vasculature and providing complementary information on cerebral perfusion. SYLVER images with a 32×32 matrix array probe at 2 MHz using diverging-wave transmits. CE and FDA clearances are expected in 2026. This directory holds the data Resolve Stroke contributes to OpenH-RF: pre-beamformed RF/IQ channel data from an imaging phantom, a flow phantom, and in-vivo transcranial acquisitions, all de-identified and released under CC BY 4.0. The in-vivo data comes from the CPP-approved SCULPT clinical study. Two transmit sequences are included: a saddle sequence that images a 2D plane for real-time B-mode, and a 4 kHz single-aperture volume sequence for CEUS and blood-flow measurement.
+
+Each dataset directory holds its data card (`README.md`), a `reconstruct.py` + `pipeline.yaml` beamforming recipe and, for the CEUS datasets, a power-Doppler reconstruction; the HDF5 files are on the Hub and the preview images in [`assets/`](assets/).
+
+## Dataset Contributor(s)
+
+- Aitana Waelbroeck
+- Carl Ferlay
+- Arthur Chavignon
+- Maxence Reberol <maxence.reberol@resolvestroke.com> (contact)
+- Vincent Hingot
+- Resolve Stroke, 29 Rue du Faubourg Saint-Jacques, 75014 Paris
+
+## Dataset Creation Date
+
+01/23/2026 – 07/09/2026 (per sub-dataset; see each data card).
+
+## License / Terms of Use
+
+[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode.en). Retain attribution and identify modifications when reusing the data.
+
+## Processing the Dataset
+
+Each sub-dataset has its own `reconstruct.py` and `pipeline.yaml`, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF/tree/main/datasets/resolvestroke), built on the [zea library](https://github.com/tue-bmd/zea); the CEUS datasets add a power-Doppler reconstruction. See the data card of each sub-dataset for how to run them.
+
+The Python scripts carry their own `SPDX-License-Identifier: Apache-2.0` header; the dataset itself is CC BY 4.0.
+
+## Dataset Format
+
+[zea v0.1.6](https://github.com/tue-bmd/zea)
+
+## Dataset Quantification
 
 **Current OpenH-RF release:** 43 HDF5 files; 110.62 GB (110,619,394,048 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
 
@@ -63,16 +76,9 @@ reconstruction; the HDF5 files are on the Hub and the preview images in
 
 ## `clinical/` — transcranial CEUS, 20 acquisitions
 
-Ten human subjects of the SCULPT study, two acquisitions each (other side and/or
-second session), imaged through the temporal acoustic window during a microbubble
-bolus. Each file holds 20 000 frames of diverging-wave IQ channel data at 4 kHz:
-five 1 s clips taken before the bolus and at +5, +10, +15 and +20 s. Every file also
-carries reference maps computed by SYLVER from the complete bolus passage
-(`custom/computed_references/`: microvascular image and radial velocities on a 0.6 mm
-Cartesian grid), provided as targets, not as a clinical ground truth.
+Ten human subjects of the SCULPT study, two acquisitions each (other side and/or second session), imaged through the temporal acoustic window during a microbubble bolus. Each file holds 20 000 frames of diverging-wave IQ channel data at 4 kHz: five 1 s clips taken before the bolus and at +5, +10, +15 and +20 s. Every file also carries reference maps computed by SYLVER from the complete bolus passage (`custom/computed_references/`: microvascular image and radial velocities on a 0.6 mm Cartesian grid), provided as targets, not as a clinical ground truth.
 
-Power Doppler reconstructed from the released channel data, one x-z projection per
-acquisition at bolus +10 s:
+Power Doppler reconstructed from the released channel data, one x-z projection per acquisition at bolus +10 s:
 
 <a href="assets/clinical_pd_montage.png"><img src="assets/clinical_pd_montage.png" alt="Power Doppler x-z MIP at bolus +10 s for the 20 clinical acquisitions" width="100%"></a>
 
@@ -80,16 +86,11 @@ The reference microvascular image of the same 20 acquisitions, x-z projection:
 
 <a href="assets/clinical_mvi_montage.png"><img src="assets/clinical_mvi_montage.png" alt="Reference microvascular image (mvi), x-z MIP, for the 20 clinical acquisitions" width="100%"></a>
 
-The [data card](clinical/README.md) has the inventory of the 20 acquisitions with the
-five-clip power Doppler and the reference maps of each, the field table, and the
-reconstruction scripts.
+The [data card](clinical/README.md) has the inventory of the 20 acquisitions with the five-clip power Doppler and the reference maps of each, the field table, and the reconstruction scripts.
 
 ## `phantom_flow/` — flow phantom CEUS
 
-A CIRS 769 flow phantom with an ATS523A pump, microbubbles flowing through two tubes
-(4 mm and 2 mm), same sequence and clip structure as the clinical files. The file also
-carries the tube mask (known geometry, an actual ground truth) and the same computed
-reference maps as the clinical data.
+A CIRS 769 flow phantom with an ATS523A pump, microbubbles flowing through two tubes (4 mm and 2 mm), same sequence and clip structure as the clinical files. The file also carries the tube mask (known geometry, an actual ground truth) and the same computed reference maps as the clinical data.
 
 <a href="assets/phantom_flow_bmode.png"><img src="assets/phantom_flow_bmode.png" alt="phantom_flow B-mode, two perpendicular sectors" width="100%"></a>
 
@@ -107,9 +108,7 @@ See the [data card](phantom_flow/README.md).
 
 ## `phantom_mp/` — multi-tissue imaging phantom
 
-A CIRS 040GSE phantom, 1000 volumetric frames of the same static scene with the
-4 kHz diverging-wave sequence: wire targets, cysts and tissue-mimicking background,
-for beamforming and resolution studies.
+A CIRS 040GSE phantom, 1000 volumetric frames of the same static scene with the 4 kHz diverging-wave sequence: wire targets, cysts and tissue-mimicking background, for beamforming and resolution studies.
 
 <a href="assets/phantom_mp_bmode.png"><img src="assets/phantom_mp_bmode.png" alt="phantom_mp B-mode, two perpendicular sectors" width="100%"></a>
 
@@ -119,20 +118,10 @@ See the [data card](phantom_mp/README.md).
 
 ## `saddle/` — anatomical reference B-modes
 
-One wide-angle diverging-wave frame per acquisition from the "saddle" sequence
-(9 transmits steered −24° to +24°; each transmit is repeated for four consecutive
-256-element receive sub-apertures, so the frame carries the full 1024-element
-aperture), for the 20 clinical acquisitions and the imaging phantom: the anatomical
-view at the same probe placement as the CEUS clips.
+One wide-angle diverging-wave frame per acquisition from the "saddle" sequence (9 transmits steered −24° to +24°; each transmit is repeated for four consecutive 256-element receive sub-apertures, so the frame carries the full 1024-element aperture), for the 20 clinical acquisitions and the imaging phantom: the anatomical view at the same probe placement as the CEUS clips.
 
 <a href="assets/saddle_bmode_montage.png"><img src="assets/saddle_bmode_montage.png" alt="Saddle-array B-modes for the 21 files" width="100%"></a>
 
 *Figure: the 21 saddle B-modes.*
 
 See the [data card](saddle/README.md).
-
-## Contributors
-
-Aitana Waelbroeck\*, Carl Ferlay\*, Arthur Chavignon\*, Maxence Reberol\*, Vincent Hingot\*
-
-_\*Resolve Stroke, 29 Rue du Faubourg Saint-Jacques, 75014 Paris_

resolvestroke/clinical/README.md

@@ -1,5 +1,6 @@
 ---
-pretty_name: "OpenH-RF - Resolve Stroke Clinical Transcranial CEUS (SCULPT, 20 acquisitions)"
+name: resolvestroke-clinical
+pretty_name: "Resolve Stroke Clinical Transcranial CEUS (SCULPT, 20 acquisitions)"
 license: cc-by-4.0
 task_categories:
   - other
@@ -19,22 +20,17 @@ size_categories:
   - 100K<n<1M
 ---
 
-# OpenH-RF - Resolve Stroke Clinical Transcranial CEUS (SCULPT, 20 acquisitions)
+# Resolve Stroke Clinical Transcranial CEUS (SCULPT, 20 acquisitions)
 
 <img src="../assets/clinical_mvi_montage.png" alt="Microvascular image (x-z MIP of the computed reference) for the 20 clinical acquisitions" width="100%">
 
+*Reference microvascular image (`mvi`, x-z maximum-intensity projection of the computed reference) for the 20 acquisitions in [`clinical/`](https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical).*
 
 ## Dataset Description
 
-Contrast-enhanced ultrasound (CEUS) transcranial brain acquisitions of 10 human
-subjects (20 acquisitions), acquired with SYLVER, Resolve Stroke's ultrasound device,
-using a 32×32 matrix probe with diverging-wave transmits at 4 kHz frame rate. Each
-acquisition captures a microbubble contrast-agent bolus wash-in: microbubbles are
-injected, perfuse the brain vasculature through the temporal acoustic window, and
-gradually wash out.
+Contrast-enhanced ultrasound (CEUS) transcranial brain acquisitions of 10 human subjects (20 acquisitions), acquired with SYLVER, Resolve Stroke's ultrasound device, using a 32×32 matrix probe with diverging-wave transmits at 4 kHz frame rate. Each acquisition captures a microbubble contrast-agent bolus wash-in: microbubbles are injected, perfuse the brain vasculature through the temporal acoustic window, and gradually wash out.
 
-For each acquisition, five 1-second clips (4000 frames each, 20000 total) are
-extracted at intervals relative to the end-of-baseline marker:
+For each acquisition, five 1-second clips (4000 frames each, 20000 total) are extracted at intervals relative to the end-of-baseline marker:
 
 | Clip | Time offset | Description |
 |------|-------------|-------------|
@@ -44,17 +40,16 @@ extracted at intervals relative to the end-of-baseline marker:
 | `baseline_plus15s` | baseline + 15 s | Late wash-in |
 | `baseline_plus20s` | baseline + 20 s | Plateau / early wash-out |
 
-The anatomical companion of every acquisition, a single wide-angle B-mode frame taken
-at the same probe placement, is in the sibling [`saddle/`](../saddle/) dataset under
-the same name.
+The anatomical companion of every acquisition, a single wide-angle B-mode frame taken at the same probe placement, is in the sibling [`saddle/`](../saddle/) dataset under the same name.
 
 ## Dataset Contributor(s)
 
-Aitana Waelbroeck\*, Carl Ferlay\*, Arthur Chavignon\*, Maxence Reberol\*, Vincent Hingot\*
-
-\* Resolve Stroke (29 Rue du Faubourg Saint-Jacques, 75014 Paris)
-
-Contact email: maxence.reberol@resolvestroke.com
+- Aitana Waelbroeck
+- Carl Ferlay
+- Arthur Chavignon
+- Maxence Reberol <maxence.reberol@resolvestroke.com> (contact)
+- Vincent Hingot
+- Resolve Stroke, 29 Rue du Faubourg Saint-Jacques, 75014 Paris
 
 ## Dataset Creation Date
 
@@ -62,53 +57,41 @@ Contact email: maxence.reberol@resolvestroke.com
 
 ## License / Terms of Use
 
-CC BY 4.0. Data is released under Creative Commons Attribution 4.0 International,
-which permits commercial use with attribution. (The Python scripts in this directory
-carry their own `SPDX-License-Identifier: Apache-2.0` header; the dataset itself is
-CC BY 4.0.)
+[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode.en). Retain attribution and identify modifications when reusing the data.
 
 ## Intended Usage
 
-Contrast-enhanced ultrasound flow/perfusion quantification, microbubble detection
-and tracking, power-Doppler and CEUS transcranial imaging, and matrix-probe
-diverging-wave beamforming research (RFP task group 6.2, Blood Flow).
+Contrast-enhanced ultrasound flow/perfusion quantification, microbubble detection and tracking, power-Doppler and CEUS transcranial imaging, and matrix-probe diverging-wave beamforming research (RFP task group 6.2, Blood Flow).
 
 ## Dataset Characterization
 
-- Data collection method: 10 human subjects, 20 acquisitions (2 per subject: the
-  other side and/or a second session), transcranial CEUS with microbubble contrast
-  agent (bolus injection), acquired through the temporal acoustic window.
-- Labeling method: Automatic, no manual annotation. Per-frame clip labels
-  (`metadata/annotations/label`, one of the five clip names above) are assigned from
-  the acquisition time relative to the end-of-baseline marker. Per-voxel reference maps
-  (`mvi`, radial velocities) are generated by SYLVER's processing pipeline from the
-  complete bolus passage (see [Computed References](#computed-references)).
-- Acquisition system: SYLVER (Resolve Stroke's ultrasound device). 32×32 matrix
-  probe, 0.50 mm pitch, 0.30 mm kerf; transmit center frequency ≈ 2.031 MHz, sound
-  speed 1540 m/s. Diverging-wave transmits; receive sub-apertures flattened into 256
-  virtual elements. Channel data is DDC (baseband) IQ, so `sampling_frequency`
-  (≈ 2.031 MHz) is the post-decimation IQ rate and equals `demodulation_frequency`.
+- Data collection method: 10 human subjects, 20 acquisitions (2 per subject: the other side and/or a second session), transcranial CEUS with microbubble contrast agent (bolus injection), acquired through the temporal acoustic window.
+- Labeling method: Automatic, no manual annotation. Per-frame clip labels (`metadata/annotations/label`, one of the five clip names above) are assigned from the acquisition time relative to the end-of-baseline marker. Per-voxel reference maps (`mvi`, radial velocities) are generated by SYLVER's processing pipeline from the complete bolus passage (see [Computed References](#computed-references)).
+- Acquisition system: SYLVER (Resolve Stroke's ultrasound device). 32×32 matrix probe, 0.50 mm pitch, 0.30 mm kerf; transmit center frequency ≈ 2.031 MHz, sound speed 1540 m/s. Diverging-wave transmits; receive sub-apertures flattened into 256 virtual elements. Channel data is DDC (baseband) IQ, so `sampling_frequency` (≈ 2.031 MHz) is the post-decimation IQ rate and equals `demodulation_frequency`.
+
+## Processing the Dataset
+
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/resolvestroke/clinical/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
+
+The reconstruction recipe is identical for all 20 acquisitions, so the scripts and pipeline YAMLs live once in this directory. Each script has a `SUBJECT` constant at the top (default `"SP02-Left-2"`); set it to any acquisition name from the inventory, e.g. `SUBJECT = "SP07-Right"`, and the `hf://` input path `hf://nvidia/OpenH-RF/resolvestroke/clinical/<SUBJECT>/<SUBJECT>.hdf5` and the output filenames follow from it. Swap `ZEA_FILE` (`INPUT` in `reconstruct_PD_3d.py`) for a local path to run against your own copy.
+
+```bash
+uv run --project /path/to/OpenH-RF python reconstruct.py
+```
+
+`reconstruct_PD_3d.py` (with `pipeline_PD_3d.yaml`) renders the power-Doppler reconstruction and `display_references.py` the computed reference maps; both are described below.
+
+The Python scripts carry their own `SPDX-License-Identifier: Apache-2.0` header; the dataset itself is CC BY 4.0.
 
 ## Dataset Format
 
-One zea HDF5 file per acquisition, `<name>/<name>.hdf5`, containing all 5 clips
-concatenated. Files are named `<subject>-<side>[-<session>]` (anonymized subject
-code `SP01`–`SP10`, imaging side, and a sequential index when a subject has two
-acquisitions on the same side). Gaps between clips are encoded in
-`scan/time_to_next_transmit`. Per-frame clip labels are stored in
-`metadata/annotations/label`.
-
-Computed reference maps (derived from the SYLVER processed exam) are stored in the
-zea `custom` group under `custom/computed_references/`: `mvi`,
-`velocity_radial_avg` / `_min` / `_max`, and a shared `coordinates` array (per-voxel
-`[x, y, z]` in metres), all on one 0.6 mm Cartesian grid `(192, 171, 171)`. Read
-them via `zea.File(...).dataset("custom/computed_references/<name>")`.
-
-The files are in the zea HDF5 format, root `zea_version` 0.1.6. `data/raw_data` is DDC IQ (last axis [I, Q]). The hardware time-gain compensation is baked into `raw_data`;
-`scan/tgc_gain_curve` is the applied (non-linear) gain per axial sample; divide by
-it to recover true channel amplitudes. The reconstruction scripts divide `raw_data`
-by this curve before beamforming (the `zea.Pipeline` itself stays standard; the
-reversal is a plain array step).
+[zea v0.1.6](https://github.com/tue-bmd/zea)
+
+One zea HDF5 file per acquisition, `<name>/<name>.hdf5`, containing all 5 clips concatenated. Files are named `<subject>-<side>[-<session>]` (anonymized subject code `SP01`–`SP10`, imaging side, and a sequential index when a subject has two acquisitions on the same side). Gaps between clips are encoded in `scan/time_to_next_transmit`. Per-frame clip labels are stored in `metadata/annotations/label`.
+
+Computed reference maps (derived from the SYLVER processed exam) are stored in the zea `custom` group under `custom/computed_references/`: `mvi`, `velocity_radial_avg` / `_min` / `_max`, and a shared `coordinates` array (per-voxel `[x, y, z]` in metres), all on one 0.6 mm Cartesian grid `(192, 171, 171)`. Read them via `zea.File(...).dataset("custom/computed_references/<name>")`.
+
+The files are in the zea HDF5 format, root `zea_version` 0.1.6. `data/raw_data` is DDC IQ (last axis [I, Q]). The hardware time-gain compensation is baked into `raw_data`; `scan/tgc_gain_curve` is the applied (non-linear) gain per axial sample; divide by it to recover true channel amplitudes. The reconstruction scripts divide `raw_data` by this curve before beamforming (the `zea.Pipeline` itself stays standard; the reversal is a plain array step).
 
 ## Dataset Quantification
 
@@ -140,10 +123,7 @@ reversal is a plain array step).
 
 ### Inventory
 
-One row per acquisition. Power Doppler: x-z maximum-intensity projection of each of
-the five clips (baseline, +5, +10, +15, +20 s), display window set per acquisition
-from the 90th and 99.95th percentiles of its dB values. Reference maps: `mvi`
-(microvascular image) and `velocity_radial_avg`, each as x-z and y-z projections.
+One row per acquisition. Power Doppler: x-z maximum-intensity projection of each of the five clips (baseline, +5, +10, +15, +20 s), display window set per acquisition from the 90th and 99.95th percentiles of its dB values. Reference maps: `mvi` (microvascular image) and `velocity_radial_avg`, each as x-z and y-z projections.
 
 <table width="100%">
   <tr>
@@ -174,61 +154,27 @@ from the 90th and 99.95th percentiles of its dB values. Reference maps: `mvi`
 ## Subject Metadata
 
 - Type: Human subjects (`metadata/subject/type` = `human`)
-- Subject IDs: `SP01`–`SP10` (anonymized, `metadata/subject/id`); each subject
-  contributes 2 acquisitions (see the inventory above for side and session)
+- Subject IDs: `SP01`–`SP10` (anonymized, `metadata/subject/id`); each subject contributes 2 acquisitions (see the inventory above for side and session)
 - Anatomy: Brain (transcranial), left or right temporal acoustic window
 - Contrast agent: Microbubbles (bolus injection)
 
 ## Data Validation
 
-The reconstruction recipe is identical for all 20 acquisitions, so the scripts and
-pipeline YAMLs live once in this directory. Each script has a `SUBJECT` constant at
-the top (default `"SP02-Left-2"`); set it to any acquisition name from the inventory,
-e.g. `SUBJECT = "SP07-Right"`, and the `hf://` input path
-`hf://nvidia/OpenH-RF/resolvestroke/clinical/<SUBJECT>/<SUBJECT>.hdf5` and the output
-filenames follow from it. Swap `INPUT` for a local path to run against your own copy.
-
-`reconstruct.py` first divides `raw_data` by `scan/tgc_gain_curve` (reverse TGC),
-then runs a standard `zea.Pipeline` (cast → DAS beamform → envelope → normalize →
-log-compress) defined in `pipeline.yaml` to reconstruct a B-mode from the IQ channel
-data. Because the probe is a 2D matrix array insonified by a single diverging-wave
-transmit, `reconstruct.py` beamforms on polar (sector) grids and renders two
-perpendicular sector B-modes, the x-z plane (y = 0) and the y-z plane (x = 0), side
-by side:
+`reconstruct.py` first divides `raw_data` by `scan/tgc_gain_curve` (reverse TGC), then runs a standard `zea.Pipeline` (cast → DAS beamform → envelope → normalize → log-compress) defined in `pipeline.yaml` to reconstruct a B-mode from the IQ channel data. Because the probe is a 2D matrix array insonified by a single diverging-wave transmit, `reconstruct.py` beamforms on polar (sector) grids and renders two perpendicular sector B-modes, the x-z plane (y = 0) and the y-z plane (x = 0), side by side:
 
 ![Reference B-mode (two perpendicular sectors), SP02-Left-2](../assets/clinical_SP02-Left-2_bmode.png)
 
-There is little to see in a single-frame B-mode of a transcranial CEUS acquisition:
-through the skull the diverging-wave frame is dominated by speckle and reverberation,
-and the microbubbles are not distinguishable from tissue without temporal filtering.
-The B-mode only checks the geometry and depth scaling of the reconstruction; the
-content of the data appears in the power-Doppler reconstruction below.
+There is little to see in a single-frame B-mode of a transcranial CEUS acquisition: through the skull the diverging-wave frame is dominated by speckle and reverberation, and the microbubbles are not distinguishable from tissue without temporal filtering. The B-mode only checks the geometry and depth scaling of the reconstruction; the content of the data appears in the power-Doppler reconstruction below.
 
-A display-only linear TGC (1.5 dB/cm) adds a depth-dependent dB gain to the rendered
-image, compensating the attenuation that `reverse_tgc` leaves uncompensated so deeper
-structure stays visible. It does not modify the stored data.
-
-```bash
-uv run --project /path/to/OpenH-RF python reconstruct.py
-```
+A display-only linear TGC (1.5 dB/cm) adds a depth-dependent dB gain to the rendered image, compensating the attenuation that `reverse_tgc` leaves uncompensated so deeper structure stays visible. It does not modify the stored data.
 
 ### Power-Doppler reconstruction (derived product)
 
-`reconstruct_PD_3d.py` (config `pipeline_PD_3d.yaml`) demonstrates a flow/contrast
-view. For each clip it beamforms all 4000 frames, in blocks of 250, onto a real 3D
-polar sector volume (radius × azimuth × elevation), applies a 100 Hz slow-time
-high-pass (wall) filter to suppress stationary tissue, and integrates power Doppler
-(sum of the squared envelope over the frames), then displays x-z and y-z
-maximum-intensity projections (MIPs) for all five clips in dB relative to the maximum
-over the clips (-25 to -5 dB, gamma 1.25, `hot` colormap), with the reference `mvi`
-map of the same file in the last column for comparison. The wall filter and
-power-Doppler integration are custom `zea` pipeline ops registered in the script
-(`tissue_highpass`, `power_doppler`). The hardware TGC stored in `raw_data` is kept.
+`reconstruct_PD_3d.py` (config `pipeline_PD_3d.yaml`) demonstrates a flow/contrast view. For each clip it beamforms all 4000 frames, in blocks of 250, onto a real 3D polar sector volume (radius × azimuth × elevation), applies a 100 Hz slow-time high-pass (wall) filter to suppress stationary tissue, and integrates power Doppler (sum of the squared envelope over the frames), then displays x-z and y-z maximum-intensity projections (MIPs) for all five clips in dB relative to the maximum over the clips (-25 to -5 dB, gamma 1.25, `hot` colormap), with the reference `mvi` map of the same file in the last column for comparison. The wall filter and power-Doppler integration are custom `zea` pipeline ops registered in the script (`tissue_highpass`, `power_doppler`). The hardware TGC stored in `raw_data` is kept.
 
 ![Power-Doppler 3D MIP montage with reference mvi, SP03-Left](../assets/clinical_SP03-Left_PD_montage.png)
 
-Run (a GPU is strongly recommended: about 14 min per acquisition with `uv sync --extra gpu`,
-hours with the CPU-only JAX of the plain `uv sync`):
+Run (a GPU is strongly recommended: about 14 min per acquisition with `uv sync --extra gpu`, hours with the CPU-only JAX of the plain `uv sync`):
 
 ```bash
 uv run --project /path/to/OpenH-RF python reconstruct_PD_3d.py
@@ -236,18 +182,9 @@ uv run --project /path/to/OpenH-RF python reconstruct_PD_3d.py
 
 ## Computed References
 
-Reference maps derived from the SYLVER processed exam of each acquisition, stored
-in `custom/computed_references/` (read via
-`zea.File(...).dataset("custom/computed_references/<name>")`). All share one
-0.6 mm Cartesian grid `(192, 171, 171)`; the `coordinates` array gives each voxel's
-`[x, y, z]` in metres.
+Reference maps derived from the SYLVER processed exam of each acquisition, stored in `custom/computed_references/` (read via `zea.File(...).dataset("custom/computed_references/<name>")`). All share one 0.6 mm Cartesian grid `(192, 171, 171)`; the `coordinates` array gives each voxel's `[x, y, z]` in metres.
 
-The reference maps were computed by SYLVER's proprietary processing pipeline from
-the complete bolus passage, not from the five 1 s clips released here. They are
-therefore not reproducible from the released frames, and are provided as reference
-targets, for instance for learning-based reconstruction, microbubble-flow or perfusion
-estimation from the channel data. They are a device output, not a clinically validated
-ground truth.
+The reference maps were computed by SYLVER's proprietary processing pipeline from the complete bolus passage, not from the five 1 s clips released here. They are therefore not reproducible from the released frames, and are provided as reference targets, for instance for learning-based reconstruction, microbubble-flow or perfusion estimation from the channel data. They are a device output, not a clinically validated ground truth.
 
 | Field | dtype | Unit | Description |
 |---|---|---|---|
@@ -259,17 +196,10 @@ ground truth.
 
 Notes:
 
-- Velocities are radial (Doppler, along-beam) components. True flow speed depends on
-  the (subject- and vessel-dependent) insonation angle, which is not known a priori
-  for in-vivo cerebral vasculature.
+- Velocities are radial (Doppler, along-beam) components. True flow speed depends on the (subject- and vessel-dependent) insonation angle, which is not known a priori for in-vivo cerebral vasculature.
 - `mvi` and the velocity maps are NaN outside the sonified cone.
 
-`display_references.py` renders all reference maps of one acquisition to a montage.
-It reads `custom/computed_references/` (no
-beamforming) and, for each map, shows two orthogonal maximum-intensity projections
-(x-z on top, y-z below) on the true Cartesian geometry: `mvi` in magma and the
-velocities in a symmetric blue-white-red map (±0.76 m/s). It takes `SUBJECT` or
-`--input <hf:// or local path>`:
+`display_references.py` renders all reference maps of one acquisition to a montage. It reads `custom/computed_references/` (no beamforming) and, for each map, shows two orthogonal maximum-intensity projections (x-z on top, y-z below) on the true Cartesian geometry: `mvi` in magma and the velocities in a symmetric blue-white-red map (±0.76 m/s). It takes `SUBJECT` or `--input <hf:// or local path>`:
 
 ![Computed reference maps montage, SP02-Left-2](../assets/clinical_SP02-Left-2_references_montage.png)
 
@@ -279,23 +209,12 @@ uv run --project /path/to/OpenH-RF python display_references.py
 
 ## Ethical Considerations
 
-Human-subject data. Channel data was acquired during the SCULPT clinical study
-(National registration number (ID RCB): 2025-A00023-46; NCT07324421), a prospective
-monocentric trial conducted at CHU Gui de Chauliac (Montpellier, France) under approval
-from the French ethics committee (Comité de Protection des Personnes, CPP), comparing
-cerebral perfusion from Resolve Stroke's SYLVER ultrasound system with routine perfusion
-CT in ICU/CCU patients, using SonoVue® as the contrast agent.
+Human-subject data. Channel data was acquired during the SCULPT clinical study (National registration number (ID RCB): 2025-A00023-46; NCT07324421), a prospective monocentric trial conducted at CHU Gui de Chauliac (Montpellier, France) under approval from the French ethics committee (Comité de Protection des Personnes, CPP), comparing cerebral perfusion from Resolve Stroke's SYLVER ultrasound system with routine perfusion CT in ICU/CCU patients, using SonoVue® as the contrast agent.
 
-The dataset contains no direct personal identifiers: subjects are referenced only by
-an anonymized study code, and no name, date of birth, or device/operator identifiers
-are stored in the released files.
+The dataset contains no direct personal identifiers: subjects are referenced only by an anonymized study code, and no name, date of birth, or device/operator identifiers are stored in the released files.
 
 ## Known Issues
 
-- `raw_data` has the hardware TGC baked in; `scan/tgc_gain_curve` is that (non-linear)
-  applied curve. The reconstruction scripts divide by the curve before beamforming;
-  a downstream user reconstructing directly must divide by the curve too.
-- `sampling_frequency ≈ center_frequency` because the data is DDC baseband IQ (see
-  Dataset Characterization), not an RF acquisition.
-- Radial velocity maps are along-beam only; absolute speed requires the unknown
-  local vessel directions.
+- `raw_data` has the hardware TGC baked in; `scan/tgc_gain_curve` is that (non-linear) applied curve. The reconstruction scripts divide by the curve before beamforming; a downstream user reconstructing directly must divide by the curve too.
+- `sampling_frequency ≈ center_frequency` because the data is DDC baseband IQ (see Dataset Characterization), not an RF acquisition.
+- Radial velocity maps are along-beam only; absolute speed requires the unknown local vessel directions.

resolvestroke/phantom_flow/README.md

@@ -1,5 +1,6 @@
 ---
-pretty_name: "OpenH-RF - Resolve Stroke Flow Phantom (CIRS 769 + ATS523A) On/Off"
+name: resolvestroke-phantom-flow
+pretty_name: "Resolve Stroke Flow Phantom (CIRS 769 + ATS523A) On/Off"
 license: cc-by-4.0
 task_categories:
   - other
@@ -18,18 +19,17 @@ size_categories:
   - 10K<n<100K
 ---
 
-# OpenH-RF - Resolve Stroke Flow Phantom (CIRS 769 + ATS523A) On/Off
+# Resolve Stroke Flow Phantom (CIRS 769 + ATS523A) On/Off
+
+![Power-Doppler 3D MIP montage with reference mvi](../assets/phantom_flow_PD_montage.png)
+
+*Power Doppler of the five clips (x-z and y-z maximum-intensity projections), with the reference `mvi` in the last column, reconstructed from [`phantom_flow.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/resolvestroke/phantom_flow/phantom_flow.hdf5).*
 
 ## Dataset Description
 
-Contrast-enhanced ultrasound (CEUS) acquisition of a flow phantom (CIRS 769 + ATS523A)
-with microbubble contrast agent. Acquired with SYLVER, Resolve Stroke's ultrasound
-device, using a 32×32 matrix probe with diverging-wave transmits at 4 kHz frame
-rate. The acquisition captures a flow-on/flow-off bolus wash-in scenario: microbubbles
-are injected, flow through the phantom pipe, and gradually wash out.
+Contrast-enhanced ultrasound (CEUS) acquisition of a flow phantom (CIRS 769 + ATS523A) with microbubble contrast agent. Acquired with SYLVER, Resolve Stroke's ultrasound device, using a 32×32 matrix probe with diverging-wave transmits at 4 kHz frame rate. The acquisition captures a flow-on/flow-off bolus wash-in scenario: microbubbles are injected, flow through the phantom pipe, and gradually wash out.
 
-Five 1-second clips (4000 frames each, 20000 total) are extracted at intervals
-relative to the end-of-baseline marker:
+Five 1-second clips (4000 frames each, 20000 total) are extracted at intervals relative to the end-of-baseline marker:
 
 | Clip | Time offset | Description |
 |------|-------------|-------------|
@@ -41,11 +41,12 @@ relative to the end-of-baseline marker:
 
 ## Dataset Contributor(s)
 
-Aitana Waelbroeck\*, Carl Ferlay\*, Arthur Chavignon\*, Maxence Reberol\*, Vincent Hingot\*
-
-\* Resolve Stroke (29 Rue du Faubourg Saint-Jacques, 75014 Paris)
-
-Contact email: maxence.reberol@resolvestroke.com
+- Aitana Waelbroeck
+- Carl Ferlay
+- Arthur Chavignon
+- Maxence Reberol <maxence.reberol@resolvestroke.com> (contact)
+- Vincent Hingot
+- Resolve Stroke, 29 Rue du Faubourg Saint-Jacques, 75014 Paris
 
 ## Dataset Creation Date
 
@@ -53,53 +54,39 @@ Contact email: maxence.reberol@resolvestroke.com
 
 ## License / Terms of Use
 
-CC BY 4.0 (see `LICENCE`). Data is released under Creative Commons Attribution
-4.0 International, which permits commercial use with attribution. (The Python
-scripts in this directory carry their own `SPDX-License-Identifier: Apache-2.0`
-header; the dataset itself is CC BY 4.0.)
+[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode.en). Retain attribution and identify modifications when reusing the data.
 
 ## Intended Usage
 
-Contrast-enhanced ultrasound flow quantification, microbubble detection and
-tracking, power-Doppler and CEUS flow imaging, and matrix-probe diverging-wave
-beamforming research (RFP task group 6.2, Blood Flow).
+Contrast-enhanced ultrasound flow quantification, microbubble detection and tracking, power-Doppler and CEUS flow imaging, and matrix-probe diverging-wave beamforming research (RFP task group 6.2, Blood Flow).
 
 ## Dataset Characterization
 
 - Data collection method: Phantom (CIRS 769 + ATS523A flow phantom with microbubble contrast agent)
-- Labeling method: Automatic, no manual annotation. The per-voxel tube mask comes from
-  the known phantom geometry; the reference maps (`mvi`, radial velocities) are generated
-  by SYLVER's processing pipeline from the complete bolus passage (see
-  [Computed References](#computed-references)). Per-frame clip labels
-  (`metadata/annotations/label`, one of the five clip names above) are assigned from
-  the acquisition time relative to the end-of-baseline marker.
-- Acquisition system: SYLVER (Resolve Stroke's ultrasound device). 32×32 matrix
-  probe, 0.50 mm pitch, 0.30 mm kerf; transmit center frequency ≈ 2.031 MHz, sound
-  speed 1540 m/s. Diverging-wave transmits; receive sub-apertures flattened into 256
-  virtual elements. Channel data is DDC (baseband) IQ, so `sampling_frequency`
-  (≈ 2.031 MHz) is the post-decimation IQ rate and equals `demodulation_frequency`.
+- Labeling method: Automatic, no manual annotation. The per-voxel tube mask comes from the known phantom geometry; the reference maps (`mvi`, radial velocities) are generated by SYLVER's processing pipeline from the complete bolus passage (see [Computed References](#computed-references)). Per-frame clip labels (`metadata/annotations/label`, one of the five clip names above) are assigned from the acquisition time relative to the end-of-baseline marker.
+- Acquisition system: SYLVER (Resolve Stroke's ultrasound device). 32×32 matrix probe, 0.50 mm pitch, 0.30 mm kerf; transmit center frequency ≈ 2.031 MHz, sound speed 1540 m/s. Diverging-wave transmits; receive sub-apertures flattened into 256 virtual elements. Channel data is DDC (baseband) IQ, so `sampling_frequency` (≈ 2.031 MHz) is the post-decimation IQ rate and equals `demodulation_frequency`.
+
+## Processing the Dataset
+
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/resolvestroke/phantom_flow/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
+
+```bash
+uv run --project /path/to/OpenH-RF python reconstruct.py
+```
+
+`reconstruct_PD_3d.py` (with `pipeline_PD_3d.yaml`) renders the power-Doppler reconstruction and `display_references.py` the computed reference maps; both are described below.
+
+The Python scripts carry their own `SPDX-License-Identifier: Apache-2.0` header; the dataset itself is CC BY 4.0.
 
 ## Dataset Format
 
-Single zea HDF5 file (`phantom_flow.hdf5`) containing all 5 clips concatenated.
-Gaps between clips are encoded in `scan/time_to_next_transmit`. Per-frame clip
-labels are stored in `metadata/annotations/label`.
-
-Computed reference maps (derived from the SYLVER processed exam) are stored in the
-zea `custom` group under `custom/computed_references/`: `mvi`,
-`velocity_radial_avg` / `_min` / `_max`, `tube_mask`, and a shared `coordinates`
-array (per-voxel `[x, y, z]` in metres), all on one 0.6 mm Cartesian grid
-`(192, 171, 171)`. Read them via `zea.File(...).custom`. They live in `custom`
-rather than a second track because zea uses a single global `n_frames`: a
-single-frame reference map cannot share that dimension with the per-frame
-`metadata/annotations/label` (20000), so a reference track fails spec validation.
-
-The file is in the zea HDF5 format, root `zea_version` 0.1.6, validated `compliant: true` against `validate_zea_spec.py`.
-`data/raw_data` is DDC IQ (last axis [I, Q]). The hardware
-time-gain compensation is baked into `raw_data`; `scan/tgc_gain_curve` is the applied
-(non-linear) gain per axial sample; divide by it to recover true channel amplitudes.
-The reconstruction scripts divide `raw_data` by this curve before beamforming (the
-`zea.Pipeline` itself stays standard; the reversal is a plain array step).
+[zea v0.1.6](https://github.com/tue-bmd/zea)
+
+Single zea HDF5 file (`phantom_flow.hdf5`) containing all 5 clips concatenated. Gaps between clips are encoded in `scan/time_to_next_transmit`. Per-frame clip labels are stored in `metadata/annotations/label`.
+
+Computed reference maps (derived from the SYLVER processed exam) are stored in the zea `custom` group under `custom/computed_references/`: `mvi`, `velocity_radial_avg` / `_min` / `_max`, `tube_mask`, and a shared `coordinates` array (per-voxel `[x, y, z]` in metres), all on one 0.6 mm Cartesian grid `(192, 171, 171)`. Read them via `zea.File(...).custom`. They live in `custom` rather than a second track because zea uses a single global `n_frames`: a single-frame reference map cannot share that dimension with the per-frame `metadata/annotations/label` (20000), so a reference track fails spec validation.
+
+The file is in the zea HDF5 format, root `zea_version` 0.1.6, validated `compliant: true` against `validate_zea_spec.py`. `data/raw_data` is DDC IQ (last axis [I, Q]). The hardware time-gain compensation is baked into `raw_data`; `scan/tgc_gain_curve` is the applied (non-linear) gain per axial sample; divide by it to recover true channel amplitudes. The reconstruction scripts divide `raw_data` by this curve before beamforming (the `zea.Pipeline` itself stays standard; the reversal is a plain array step).
 
 ## Dataset Quantification
 
@@ -136,35 +123,17 @@ The reconstruction scripts divide `raw_data` by this curve before beamforming (t
 
 ## Data Validation
 
-`reconstruct.py` first divides `raw_data` by `scan/tgc_gain_curve` (reverse TGC),
-then runs a standard `zea.Pipeline` (cast → DAS beamform → envelope → normalize →
-log-compress) defined in `pipeline.yaml` to reconstruct a B-mode from the IQ channel
-data. Because the probe is a 2D matrix array insonified by a single diverging-wave
-transmit, `reconstruct.py` beamforms on polar (sector) grids and renders two
-perpendicular sector B-modes, the x-z plane (y = 0) and the y-z plane (x = 0), side
-by side:
+`reconstruct.py` first divides `raw_data` by `scan/tgc_gain_curve` (reverse TGC), then runs a standard `zea.Pipeline` (cast → DAS beamform → envelope → normalize → log-compress) defined in `pipeline.yaml` to reconstruct a B-mode from the IQ channel data. Because the probe is a 2D matrix array insonified by a single diverging-wave transmit, `reconstruct.py` beamforms on polar (sector) grids and renders two perpendicular sector B-modes, the x-z plane (y = 0) and the y-z plane (x = 0), side by side:
 
 ![Reference B-mode (two perpendicular sectors)](../assets/phantom_flow_bmode.png)
 
-Run: `uv run --project /path/to/OpenH-RF python reconstruct.py`
-
 ### Power-Doppler reconstruction (derived product)
 
-`reconstruct_PD_3d.py` (config `pipeline_PD_3d.yaml`) demonstrates a flow/contrast
-view. For each clip it beamforms all 4000 frames, in blocks of 250, onto a real 3D
-polar sector volume (radius × azimuth × elevation), applies a 100 Hz slow-time
-high-pass (wall) filter to suppress stationary tissue, and integrates power Doppler
-(sum of the squared envelope over the frames), then displays x-z and y-z
-maximum-intensity projections (MIPs) for all five clips in dB relative to the maximum
-over the clips (-25 to -5 dB, gamma 1.25, `hot` colormap), with the reference `mvi`
-map of the same file in the last column for comparison. The wall filter and
-power-Doppler integration are custom `zea` pipeline ops registered in the script
-(`tissue_highpass`, `power_doppler`). The hardware TGC stored in `raw_data` is kept.
+`reconstruct_PD_3d.py` (config `pipeline_PD_3d.yaml`) demonstrates a flow/contrast view. For each clip it beamforms all 4000 frames, in blocks of 250, onto a real 3D polar sector volume (radius × azimuth × elevation), applies a 100 Hz slow-time high-pass (wall) filter to suppress stationary tissue, and integrates power Doppler (sum of the squared envelope over the frames), then displays x-z and y-z maximum-intensity projections (MIPs) for all five clips in dB relative to the maximum over the clips (-25 to -5 dB, gamma 1.25, `hot` colormap), with the reference `mvi` map of the same file in the last column for comparison. The wall filter and power-Doppler integration are custom `zea` pipeline ops registered in the script (`tissue_highpass`, `power_doppler`). The hardware TGC stored in `raw_data` is kept.
 
-![Power-Doppler 3D MIP montage with reference mvi](../assets/phantom_flow_PD_montage.png)
+The resulting montage is shown at the top of this card.
 
-Run (a GPU is strongly recommended: about 14 min per acquisition with `uv sync --extra gpu`,
-hours with the CPU-only JAX of the plain `uv sync`):
+Run (a GPU is strongly recommended: about 14 min per acquisition with `uv sync --extra gpu`, hours with the CPU-only JAX of the plain `uv sync`):
 
 ```bash
 uv run --project /path/to/OpenH-RF python reconstruct_PD_3d.py
@@ -172,18 +141,9 @@ uv run --project /path/to/OpenH-RF python reconstruct_PD_3d.py
 
 ## Computed References
 
-Reference maps derived from the SYLVER processed exam of this acquisition, stored
-in `custom/computed_references/` (read via
-`zea.File(...).dataset("custom/computed_references/<name>")`). All share one
-0.6 mm Cartesian grid `(192, 171, 171)`; the `coordinates` array gives each voxel's
-`[x, y, z]` in metres.
+Reference maps derived from the SYLVER processed exam of this acquisition, stored in `custom/computed_references/` (read via `zea.File(...).dataset("custom/computed_references/<name>")`). All share one 0.6 mm Cartesian grid `(192, 171, 171)`; the `coordinates` array gives each voxel's `[x, y, z]` in metres.
 
-The reference maps were computed by SYLVER's proprietary processing pipeline from
-the complete bolus passage, not from the five 1 s clips released here. They are
-therefore not reproducible from the released frames, and are provided as reference
-targets, for instance for learning-based reconstruction, microbubble-flow or perfusion
-estimation from the channel data. They are a device output, not a clinically validated
-ground truth. `tube_mask` is the known phantom geometry and is an actual ground truth.
+The reference maps were computed by SYLVER's proprietary processing pipeline from the complete bolus passage, not from the five 1 s clips released here. They are therefore not reproducible from the released frames, and are provided as reference targets, for instance for learning-based reconstruction, microbubble-flow or perfusion estimation from the channel data. They are a device output, not a clinically validated ground truth. `tube_mask` is the known phantom geometry and is an actual ground truth.
 
 | Field | dtype | Unit | Description |
 |---|---|---|---|
@@ -195,16 +155,10 @@ ground truth. `tube_mask` is the known phantom geometry and is an actual ground
 | `coordinates` | float32 | m | Per-voxel `[x, y, z]` for all maps. |
 
 Notes:
-- Velocities are radial (Doppler, along-beam). The probe sits at ~72° to the flow,
-  so true speed ≈ radial ÷ cos(72°).
-- Tube ground truth: two straight parallel tubes fitted to the flow, ⌀4 mm and
-  ⌀2 mm, ~35 mm apart, direction ≈ (−0.95, +0.04, +0.31), spanning the imaging cone.
+- Velocities are radial (Doppler, along-beam). The probe sits at ~72° to the flow, so true speed ≈ radial ÷ cos(72°).
+- Tube ground truth: two straight parallel tubes fitted to the flow, ⌀4 mm and ⌀2 mm, ~35 mm apart, direction ≈ (−0.95, +0.04, +0.31), spanning the imaging cone.
 
-`display_references.py` renders all reference maps to a montage. It reads
-`custom/computed_references/` (via `h5py`; no beamforming) and, for each map, shows
-two orthogonal maximum-intensity projections (x-z on top, y-z below) on the true
-Cartesian geometry: `mvi` in magma, the velocities in a symmetric blue-white-red
-map (±0.76 m/s), and `tube_mask` as discrete labels (⌀4 mm, ⌀2 mm):
+`display_references.py` renders all reference maps to a montage. It reads `custom/computed_references/` (via `h5py`; no beamforming) and, for each map, shows two orthogonal maximum-intensity projections (x-z on top, y-z below) on the true Cartesian geometry: `mvi` in magma, the velocities in a symmetric blue-white-red map (±0.76 m/s), and `tube_mask` as discrete labels (⌀4 mm, ⌀2 mm):
 
 ![Computed reference maps montage](../assets/phantom_flow_references_montage.png)
 
@@ -212,11 +166,8 @@ Run: `uv run --project /path/to/OpenH-RF python display_references.py`
 
 ## Known Issues
 
-- `raw_data` has the hardware TGC baked in; `scan/tgc_gain_curve` is that (non-linear)
-  applied curve. The reconstruction scripts divide by the curve before beamforming;
-  a downstream user reconstructing directly must divide by the curve too.
-- `sampling_frequency ≈ center_frequency` because the data is DDC baseband IQ (see
-  Dataset Characterization), not an RF acquisition.
+- `raw_data` has the hardware TGC baked in; `scan/tgc_gain_curve` is that (non-linear) applied curve. The reconstruction scripts divide by the curve before beamforming; a downstream user reconstructing directly must divide by the curve too.
+- `sampling_frequency ≈ center_frequency` because the data is DDC baseband IQ (see Dataset Characterization), not an RF acquisition.
 
 ## Ethical Considerations
 

resolvestroke/phantom_mp/README.md

@@ -1,5 +1,6 @@
 ---
-pretty_name: "OpenH-RF - Resolve Stroke Multi-tissue Phantom (CIRS 040GSE)"
+name: resolvestroke-phantom-mp
+pretty_name: "Resolve Stroke Multi-tissue Phantom (CIRS 040GSE)"
 license: cc-by-4.0
 task_categories:
   - other
@@ -16,24 +17,24 @@ size_categories:
   - 1K<n<10K
 ---
 
-# OpenH-RF - Resolve Stroke Multi-tissue Phantom (CIRS 040GSE)
+# Resolve Stroke Multi-tissue Phantom (CIRS 040GSE)
 
-## Dataset Description
+![Reference B-mode (two perpendicular sectors)](../assets/phantom_mp_bmode.png)
 
-Pre-beamformed channel-data acquisition of a multi-purpose, multi-tissue imaging
-phantom (CIRS 040GSE). Acquired with SYLVER, Resolve Stroke's ultrasound device,
-using a 32×32 matrix probe with diverging-wave transmits at 4 kHz frame rate. A
-single transmit insonifies a 3D volume, so each frame is a full volumetric capture
-of the static phantom (wire targets, cysts, and tissue-mimicking background). The
-file holds 1000 consecutive frames of the same scene.
+*B-mode of one frame, x-z and y-z sectors, reconstructed from [`phantom_mp.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/resolvestroke/phantom_mp/phantom_mp.hdf5).*
 
-## Dataset Contributor(s)
+## Dataset Description
 
-Aitana Waelbroeck\*, Carl Ferlay\*, Arthur Chavignon\*, Maxence Reberol\*, Vincent Hingot\*
+Pre-beamformed channel-data acquisition of a multi-purpose, multi-tissue imaging phantom (CIRS 040GSE). Acquired with SYLVER, Resolve Stroke's ultrasound device, using a 32×32 matrix probe with diverging-wave transmits at 4 kHz frame rate. A single transmit insonifies a 3D volume, so each frame is a full volumetric capture of the static phantom (wire targets, cysts, and tissue-mimicking background). The file holds 1000 consecutive frames of the same scene.
 
-\* Resolve Stroke (29 Rue du Faubourg Saint-Jacques, 75014 Paris)
+## Dataset Contributor(s)
 
-Contact email: maxence.reberol@resolvestroke.com
+- Aitana Waelbroeck
+- Carl Ferlay
+- Arthur Chavignon
+- Maxence Reberol <maxence.reberol@resolvestroke.com> (contact)
+- Vincent Hingot
+- Resolve Stroke, 29 Rue du Faubourg Saint-Jacques, 75014 Paris
 
 ## Dataset Creation Date
 
@@ -41,37 +42,33 @@ Contact email: maxence.reberol@resolvestroke.com
 
 ## License / Terms of Use
 
-CC BY 4.0 (see `LICENCE`). Data is released under Creative Commons Attribution
-4.0 International, which permits commercial use with attribution. (The Python
-scripts in this directory carry their own `SPDX-License-Identifier: Apache-2.0`
-header; the dataset itself is CC BY 4.0.)
+[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode.en). Retain attribution and identify modifications when reusing the data.
 
 ## Intended Usage
 
-Beamforming and reconstruction research (RFP task group 6.1, Generalized
-Reconstruction): resolution and contrast assessment, compressed sensing,
-super-resolution, and matrix-probe diverging-wave 3D beamforming on a phantom with
-known target structures.
+Beamforming and reconstruction research (RFP task group 6.1, Generalized Reconstruction): resolution and contrast assessment, compressed sensing, super-resolution, and matrix-probe diverging-wave 3D beamforming on a phantom with known target structures.
 
 ## Dataset Characterization
 
 - Data collection method: Phantom (CIRS 040GSE multi-purpose, multi-tissue imaging phantom)
 - Labeling method: None (single static acquisition; no per-frame labels)
-- Acquisition system: SYLVER (Resolve Stroke's ultrasound device). 32×32 matrix
-  probe, 0.50 mm pitch, 0.30 mm kerf; transmit center frequency ≈ 2.031 MHz, sound
-  speed 1540 m/s. Diverging-wave transmits; receive sub-apertures flattened into 256
-  virtual elements. Channel data is DDC (baseband) IQ, so `sampling_frequency`
-  (≈ 2.031 MHz) is the post-decimation IQ rate and equals `demodulation_frequency`.
+- Acquisition system: SYLVER (Resolve Stroke's ultrasound device). 32×32 matrix probe, 0.50 mm pitch, 0.30 mm kerf; transmit center frequency ≈ 2.031 MHz, sound speed 1540 m/s. Diverging-wave transmits; receive sub-apertures flattened into 256 virtual elements. Channel data is DDC (baseband) IQ, so `sampling_frequency` (≈ 2.031 MHz) is the post-decimation IQ rate and equals `demodulation_frequency`.
+
+## Processing the Dataset
+
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/resolvestroke/phantom_mp/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
+
+```bash
+uv run --project /path/to/OpenH-RF python reconstruct.py
+```
+
+The Python scripts carry their own `SPDX-License-Identifier: Apache-2.0` header; the dataset itself is CC BY 4.0.
 
 ## Dataset Format
 
-Single zea HDF5 file (`phantom_mp.hdf5`), one track holding the raw channel data
-and scan parameters, in the zea HDF5 format, root `zea_version` 0.1.6, validated `compliant: true` against `validate_zea_spec.py`.
-`data/raw_data` is DDC IQ (last axis [I, Q]). The hardware time-gain compensation is baked into `raw_data`;
-`scan/tgc_gain_curve` is the applied (non-linear) gain per axial sample; divide by
-it to recover true channel amplitudes. `reconstruct.py` divides `raw_data` by this
-curve before beamforming (the `zea.Pipeline` itself stays standard; the reversal is
-a plain array step).
+[zea v0.1.6](https://github.com/tue-bmd/zea)
+
+Single zea HDF5 file (`phantom_mp.hdf5`), one track holding the raw channel data and scan parameters, in the zea HDF5 format, root `zea_version` 0.1.6, validated `compliant: true` against `validate_zea_spec.py`. `data/raw_data` is DDC IQ (last axis [I, Q]). The hardware time-gain compensation is baked into `raw_data`; `scan/tgc_gain_curve` is the applied (non-linear) gain per axial sample; divide by it to recover true channel amplitudes. `reconstruct.py` divides `raw_data` by this curve before beamforming (the `zea.Pipeline` itself stays standard; the reversal is a plain array step).
 
 ## Dataset Quantification
 
@@ -106,27 +103,13 @@ a plain array step).
 
 ## Data Validation
 
-`reconstruct.py` first divides `raw_data` by `scan/tgc_gain_curve` (reverse TGC),
-then runs a standard `zea.Pipeline` (cast → DAS beamform → envelope → normalize →
-log-compress) defined in `pipeline.yaml` to reconstruct a B-mode from the IQ channel
-data. Because the probe is a 2D matrix array insonified by a single diverging-wave
-transmit, `reconstruct.py` beamforms on polar (sector) grids and renders two
-perpendicular sector B-modes, the x-z plane (y = 0) and the y-z plane (x = 0), side
-by side:
-
-![Reference B-mode (two perpendicular sectors)](../assets/phantom_mp_bmode.png)
-
-Run: `uv run --project /path/to/OpenH-RF python reconstruct.py`
+`reconstruct.py` first divides `raw_data` by `scan/tgc_gain_curve` (reverse TGC), then runs a standard `zea.Pipeline` (cast → DAS beamform → envelope → normalize → log-compress) defined in `pipeline.yaml` to reconstruct a B-mode from the IQ channel data. Because the probe is a 2D matrix array insonified by a single diverging-wave transmit, `reconstruct.py` beamforms on polar (sector) grids and renders two perpendicular sector B-modes, the x-z plane (y = 0) and the y-z plane (x = 0), side by side, as shown at the top of this card.
 
 ## Known Issues
 
-- `raw_data` has the hardware TGC baked in; `scan/tgc_gain_curve` is that (non-linear)
-  applied curve. The reconstruction script divides by the curve before beamforming;
-  a downstream user reconstructing directly must divide by the curve too.
-- `sampling_frequency ≈ center_frequency` because the data is DDC baseband IQ (see
-  Dataset Characterization), not an RF acquisition.
-- The proposed 3D phantom-geometry reference (known target positions for the CIRS
-  040GSE) is not yet included.
+- `raw_data` has the hardware TGC baked in; `scan/tgc_gain_curve` is that (non-linear) applied curve. The reconstruction script divides by the curve before beamforming; a downstream user reconstructing directly must divide by the curve too.
+- `sampling_frequency ≈ center_frequency` because the data is DDC baseband IQ (see Dataset Characterization), not an RF acquisition.
+- The proposed 3D phantom-geometry reference (known target positions for the CIRS 040GSE) is not yet included.
 
 ## Ethical Considerations
 

resolvestroke/saddle/README.md

@@ -1,5 +1,6 @@
 ---
-pretty_name: "OpenH-RF - Resolve Stroke Saddle-Array Reference B-modes (Transcranial CEUS + Phantom)"
+name: resolvestroke-saddle
+pretty_name: "Resolve Stroke Saddle-Array Reference B-modes (Transcranial CEUS + Phantom)"
 license: cc-by-4.0
 task_categories:
   - other
@@ -19,33 +20,26 @@ size_categories:
   - n<1K
 ---
 
-# OpenH-RF - Resolve Stroke Saddle-Array Reference B-modes
+# Resolve Stroke Saddle-Array Reference B-modes
 
-## Dataset Description
+![Saddle-array B-modes for all 21 datasets](../assets/saddle_bmode_montage.png)
 
-Single-frame anatomical B-mode acquisitions from the "saddle" imaging sequence of
-Resolve Stroke's SYLVER ultrasound device, using a 32×32 matrix probe. Each raw
-acquisition contains, alongside the multi-thousand-frame contrast-enhanced
-ultrasound (CEUS) sequence, one
-wide-angle diverging-wave frame (the *saddle* sequence: `x_ang` −24°…+24° in nine
-steps, no elevation steering, cylindrical elevation focus) received on the full
-1024-element aperture through four consecutive 256-element receive events per
-transmit. Beamformed, this single frame gives a sector B-mode of the
-imaging plane: the structural reference view acquired at the same probe placement
-as the contrast (CEUS) recording.
-
-This directory contains one such reference B-mode per dataset: 20 clinical
-transcranial acquisitions (SCULPT study) and 1 static matrix-probe imaging phantom
-(21 files total). It is the structural companion to the OpenH-RF Resolve Stroke
-clinical CEUS clip submission, and the two use the same anonymized subject codes.
+*Saddle-array B-modes of all 21 files in [`saddle/data/`](https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/saddle/data), one panel per dataset; the phantom (`PMP01`) shows a regular column of point targets.*
 
-## Dataset Contributor(s)
+## Dataset Description
+
+Single-frame anatomical B-mode acquisitions from the "saddle" imaging sequence of Resolve Stroke's SYLVER ultrasound device, using a 32×32 matrix probe. Each raw acquisition contains, alongside the multi-thousand-frame contrast-enhanced ultrasound (CEUS) sequence, one wide-angle diverging-wave frame (the *saddle* sequence: `x_ang` −24°…+24° in nine steps, no elevation steering, cylindrical elevation focus) received on the full 1024-element aperture through four consecutive 256-element receive events per transmit. Beamformed, this single frame gives a sector B-mode of the imaging plane: the structural reference view acquired at the same probe placement as the contrast (CEUS) recording.
 
-Aitana Waelbroeck\*, Carl Ferlay\*, Arthur Chavignon\*, Maxence Reberol\*, Vincent Hingot\*
+This directory contains one such reference B-mode per dataset: 20 clinical transcranial acquisitions (SCULPT study) and 1 static matrix-probe imaging phantom (21 files total). It is the structural companion to the OpenH-RF Resolve Stroke clinical CEUS clip submission, and the two use the same anonymized subject codes.
 
-\* Resolve Stroke (29 Rue du Faubourg Saint-Jacques, 75014 Paris)
+## Dataset Contributor(s)
 
-Contact email: maxence.reberol@resolvestroke.com
+- Aitana Waelbroeck
+- Carl Ferlay
+- Arthur Chavignon
+- Maxence Reberol <maxence.reberol@resolvestroke.com> (contact)
+- Vincent Hingot
+- Resolve Stroke, 29 Rue du Faubourg Saint-Jacques, 75014 Paris
 
 ## Dataset Creation Date
 
@@ -53,47 +47,39 @@ Contact email: maxence.reberol@resolvestroke.com
 
 ## License / Terms of Use
 
-CC BY 4.0 (see `LICENCE`). Data is released under Creative Commons Attribution
-4.0 International, which permits commercial use with attribution. (The Python
-scripts in this directory carry their own `SPDX-License-Identifier: Apache-2.0`
-header; the dataset itself is CC BY 4.0.)
+[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode.en). Retain attribution and identify modifications when reusing the data.
 
 ## Intended Usage
 
-Matrix-probe diverging-wave beamforming research, anatomical B-mode
-reconstruction, and structural reference for the companion transcranial CEUS
-flow/perfusion datasets (OpenH-RF request-for-proposals task group 6.2, Blood Flow).
+Matrix-probe diverging-wave beamforming research, anatomical B-mode reconstruction, and structural reference for the companion transcranial CEUS flow/perfusion datasets (OpenH-RF request-for-proposals task group 6.2, Blood Flow).
 
 ## Dataset Characterization
 
-- Data collection method: 10 human subjects, 20 acquisitions (2 per subject:
-  different side and/or session), transcranial through the temporal acoustic
-  window, plus 1 static imaging phantom (wire/point targets).
+- Data collection method: 10 human subjects, 20 acquisitions (2 per subject: different side and/or session), transcranial through the temporal acoustic window, plus 1 static imaging phantom (wire/point targets).
 - Labeling method: none (a single unlabeled anatomical frame per file).
-- Acquisition system: SYLVER (Resolve Stroke's ultrasound device). SN2672 32×32
-  matrix probe, 0.50 mm pitch, 0.30 mm kerf; transmit center frequency ≈ 2.031 MHz,
-  sound speed 1540 m/s. The *saddle* sequence transmits 9 diverging waves steered
-  `x_ang` −24°…+24° (6° steps), `y_ang = 0`, virtual source at −50 mm, with an
-  elevation (saddle) focus at 120 mm. The system receives 256 channels at a time,
-  so each transmit is fired four times in a row, once per 256-element receive
-  sub-aperture; the four receptions are stacked into 1024 virtual elements
-  (element index = `aperture·256 + element`, matching PyCompute's `apElemPos`
-  ordering), giving the full probe on receive. Channel data is digital down-converted (DDC) baseband IQ, so `sampling_frequency`
-  (≈ 2.031 MHz) is the post-decimation IQ rate and equals `demodulation_frequency`.
+- Acquisition system: SYLVER (Resolve Stroke's ultrasound device). SN2672 32×32 matrix probe, 0.50 mm pitch, 0.30 mm kerf; transmit center frequency ≈ 2.031 MHz, sound speed 1540 m/s. The *saddle* sequence transmits 9 diverging waves steered `x_ang` −24°…+24° (6° steps), `y_ang = 0`, virtual source at −50 mm, with an elevation (saddle) focus at 120 mm. The system receives 256 channels at a time, so each transmit is fired four times in a row, once per 256-element receive sub-aperture; the four receptions are stacked into 1024 virtual elements (element index = `aperture·256 + element`, matching PyCompute's `apElemPos` ordering), giving the full probe on receive. Channel data is digital down-converted (DDC) baseband IQ, so `sampling_frequency` (≈ 2.031 MHz) is the post-decimation IQ rate and equals `demodulation_frequency`.
+
+## Processing the Dataset
+
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/resolvestroke/saddle/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
+
+```bash
+uv run --project /path/to/OpenH-RF python reconstruct.py
+```
+
+`reconstruct.py` streams `PMP01.hdf5` from the Hub by default; set `ZEA_FILE` at the top of the script to another of the 21 files (or a local path). The B-mode PNG is written to `assets/<file>_bmode.png`.
+
+The Python scripts carry their own `SPDX-License-Identifier: Apache-2.0` header; the dataset itself is CC BY 4.0.
 
 ## Dataset Format
 
-One zea HDF5 file per dataset under `data/`, each holding a single frame of DDC IQ
-channel data (`data/raw_data`, last axis `[I, Q]`), in the zea HDF5 format, root `zea_version` 0.1.6, validated `compliant: true` against `validate_zea_spec.py`.
+[zea v0.1.6](https://github.com/tue-bmd/zea)
+
+One zea HDF5 file per dataset under `data/`, each holding a single frame of DDC IQ channel data (`data/raw_data`, last axis `[I, Q]`), in the zea HDF5 format, root `zea_version` 0.1.6, validated `compliant: true` against `validate_zea_spec.py`.
 
-Files are named `<sp_id>[-<side>][-<n>].hdf5` (anonymized subject code, imaging
-side, and a sequential index when a subject/side has more than one acquisition);
-the phantom is `PMP01.hdf5`.
+Files are named `<sp_id>[-<side>][-<n>].hdf5` (anonymized subject code, imaging side, and a sequential index when a subject/side has more than one acquisition); the phantom is `PMP01.hdf5`.
 
-The hardware time-gain compensation is baked into `raw_data`; `scan/tgc_gain_curve`
-is the applied (non-linear) gain per axial sample. `reconstruct.py` beamforms the
-stored IQ directly (it does **not** undo the TGC, so deeper structure stays bright);
-divide `raw_data` by `scan/tgc_gain_curve` first to recover true channel amplitudes.
+The hardware time-gain compensation is baked into `raw_data`; `scan/tgc_gain_curve` is the applied (non-linear) gain per axial sample. `reconstruct.py` beamforms the stored IQ directly (it does **not** undo the TGC, so deeper structure stays bright); divide `raw_data` by `scan/tgc_gain_curve` first to recover true channel amplitudes.
 
 ## Dataset Quantification
 
@@ -124,55 +110,22 @@ divide `raw_data` by `scan/tgc_gain_curve` first to recover true channel amplitu
 ## Subject Metadata
 
 - Type: 10 human subjects (20 acquisitions) + 1 phantom
-- Subject IDs: `SP01`–`SP10` (anonymized), `PMP01` (phantom). Each subject
-  contributes 2 files (different side and/or session).
+- Subject IDs: `SP01`–`SP10` (anonymized), `PMP01` (phantom). Each subject contributes 2 files (different side and/or session).
 - Anatomy: Brain (transcranial), via the temporal acoustic window.
 
 ## Data Validation
 
-`reconstruct.py` runs a standard `zea.Pipeline` (cast → DAS beamform → envelope →
-normalize → log-compress), configured in `pipeline.yaml`, to reconstruct a B-mode
-from the IQ channel data. The probe is a 2D matrix array insonified by
-diverging-wave transmits, so it beamforms on a polar (sector) grid (a fan spanning
-the divergence angle in the x-z plane at y = 0, apex at the virtual source) and
-scan-converts the result.
-
-The montage below shows the reconstruction of all 21 files, one panel per dataset.
-The phantom (PMP01) shows a regular column of point targets, which checks the depth
-scaling and geometry.
-
-![Saddle-array B-modes for all 21 datasets](../assets/saddle_bmode_montage.png)
-
-Set up the OpenH-RF environment once (clone <https://github.com/open-h/OpenH-RF>
-and run `uv sync` in it), then reconstruct any file:
-
-```
-uv run --project /path/to/OpenH-RF python reconstruct.py
-```
+`reconstruct.py` runs a standard `zea.Pipeline` (cast → DAS beamform → envelope → normalize → log-compress), configured in `pipeline.yaml`, to reconstruct a B-mode from the IQ channel data. The probe is a 2D matrix array insonified by diverging-wave transmits, so it beamforms on a polar (sector) grid (a fan spanning the divergence angle in the x-z plane at y = 0, apex at the virtual source) and scan-converts the result.
 
-`reconstruct.py` streams `PMP01.hdf5` from the Hub by default; set `INPUT` at the top
-of the script to another of the 21 files (or a local path). The B-mode PNG is written
-next to the script as `<file>_bmode.png`.
+The montage at the top of this card shows the reconstruction of all 21 files, one panel per dataset. The phantom (PMP01) shows a regular column of point targets, which checks the depth scaling and geometry.
 
 ## Ethical Considerations
 
-Human-subject data. Channel data was acquired during the SCULPT clinical study
-(National registration number (ID RCB): 2025-A00023-46; NCT07324421), a prospective
-monocentric trial conducted at CHU Gui de Chauliac (Montpellier, France) under approval
-from the French ethics committee (Comité de Protection des Personnes, CPP), comparing
-cerebral perfusion from Resolve Stroke's SYLVER ultrasound system with routine perfusion
-CT in ICU/CCU patients, using SonoVue® as the contrast agent.
+Human-subject data. Channel data was acquired during the SCULPT clinical study (National registration number (ID RCB): 2025-A00023-46; NCT07324421), a prospective monocentric trial conducted at CHU Gui de Chauliac (Montpellier, France) under approval from the French ethics committee (Comité de Protection des Personnes, CPP), comparing cerebral perfusion from Resolve Stroke's SYLVER ultrasound system with routine perfusion CT in ICU/CCU patients, using SonoVue® as the contrast agent.
 
-The dataset contains no direct personal identifiers: subjects are referenced only by
-an anonymized study code, and no name, date of birth, or operator identifiers are
-stored in the released files. The only hardware field is the probe model (`SN2672`),
-which is identical across all files and identifies the study device, not any subject.
+The dataset contains no direct personal identifiers: subjects are referenced only by an anonymized study code, and no name, date of birth, or operator identifiers are stored in the released files. The only hardware field is the probe model (`SN2672`), which is identical across all files and identifies the study device, not any subject.
 
 ## Known Issues
 
-- `raw_data` has the hardware TGC baked in; `scan/tgc_gain_curve` is that (non-linear)
-  applied curve. `reconstruct.py` beamforms the stored IQ as-is (leaving the TGC in,
-  which keeps deep structure bright); a user wanting true channel amplitudes must
-  divide `raw_data` by the curve.
-- `sampling_frequency ≈ center_frequency` because the data is DDC baseband IQ (see
-  Dataset Characterization), not an RF acquisition.
+- `raw_data` has the hardware TGC baked in; `scan/tgc_gain_curve` is that (non-linear) applied curve. `reconstruct.py` beamforms the stored IQ as-is (leaving the TGC in, which keeps deep structure bright); a user wanting true channel amplitudes must divide `raw_data` by the curve.
+- `sampling_frequency ≈ center_frequency` because the data is DDC baseband IQ (see Dataset Characterization), not an RF acquisition.