concordia: restructure the data card to the common layout

concordia/README.md

@@ -1,5 +1,6 @@
 ---
-pretty_name: "OpenH-RF — SynthUS-FSA"
+name: concordia
+pretty_name: "SynthUS-FSA"
 license: cc-by-4.0
 task_categories:
   - image-to-image
@@ -20,50 +21,20 @@ size_categories:
 
 ![B-mode reconstructions of the five SynthUS-FSA phantom classes](assets/classes.png)
 
-The five phantom classes, each reconstructed from `data/raw_data`:
-`image_0005`, `image_0470`, `image_0640`, `image_1100`, `image_1808`.
+*The five phantom classes (anechoic, hypoechoic, hyperechoic, diverse, point-target), each reconstructed from `data/raw_data`: [`image_0005`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/concordia/data/image_0005.hdf5), [`image_0470`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/concordia/data/image_0470.hdf5), [`image_0640`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/concordia/data/image_0640.hdf5), [`image_1100`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/concordia/data/image_1100.hdf5), [`image_1808`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/concordia/data/image_1808.hdf5).*
 
-`zea` renders any capture straight from the Hub with the
-`pipeline.yaml` in this folder. Try it out with the following command:
+## Dataset Description
 
-```bash
-zea process \
-  --dataset hf://nvidia/OpenH-RF/concordia/data/image_0005.hdf5 \
-  --config hf://nvidia/OpenH-RF/concordia/pipeline.yaml \
-  --n-frames 1 \
-  --save-as png
-```
+SynthUS-FSA is a fully synthetic corpus of pre-beamformed full-synthetic-aperture (FSA) ultrasound channel-data captures, generated with the Field II simulator to model a 128-element L11-5v linear array. Each capture fires one transmit event per element (128 transmits, receive on all 128 elements), so every element-to-element combination is retained and any receive/transmit beamforming scheme (focused B-mode, multi-angle plane-wave compounding, diverging-wave, adaptive/compressive schemes) can be retrospectively synthesized from the same channel data. The dataset is packaged in [zea](https://zea.readthedocs.io) format — the OpenH-RF reference Python library for ultrasound file I/O and beamforming — which every `.hdf5` file and `reconstruct.py` depend on. The dataset targets three OpenH-RF task categories: (1) generalized reconstruction (FSA channel data paired with a synthesized full synthetic-aperture B-mode as ground truth), (2) compressed sensing / adaptive transmit (retrospective sub-selection of the 128 transmit events — derived from the same captures, no additional files), and (3) segmentation (binary echogenicity masks on a subset of phantoms). All data is simulated — no clinical, phantom-hardware, or animal acquisition is involved.
 
-## Dataset Description
+A similar data-generation approach was used in Sharifzadeh et al. (2024). Users of this dataset are kindly requested to cite that work:
 
-SynthUS-FSA is a fully synthetic corpus of pre-beamformed full-synthetic-aperture (FSA)
-ultrasound channel-data captures, generated with the Field II simulator to model a
-128-element L11-5v linear array. Each capture fires one transmit event per element
-(128 transmits, receive on all 128 elements), so every element-to-element combination
-is retained and any receive/transmit beamforming scheme (focused B-mode, multi-angle
-plane-wave compounding, diverging-wave, adaptive/compressive schemes) can be
-retrospectively synthesized from the same channel data. The dataset is packaged in
-[zea](https://zea.readthedocs.io) format — the OpenH-RF reference Python library for
-ultrasound file I/O and beamforming — which every `.hdf5` file and `reconstruct.py`
-depend on. The dataset targets three
-OpenH-RF task categories: (1) generalized reconstruction (FSA channel data paired with
-a synthesized full synthetic-aperture B-mode as ground truth), (2) compressed sensing /
-adaptive transmit (retrospective sub-selection of the 128 transmit events — derived
-from the same captures, no additional files), and (3) segmentation (binary
-echogenicity masks on a subset of phantoms). All data is simulated — no clinical,
-phantom-hardware, or animal acquisition is involved.
-
-A similar data-generation approach was used in Sharifzadeh et al. (2024). Users of
-this dataset are kindly requested to cite that work:
-
-> M. Sharifzadeh, S. Goudarzi, A. Tang, H. Benali, and H. Rivaz, "Mitigating
-> aberration-induced noise: A deep learning-based aberration-to-aberration approach,"
-> *IEEE Transactions on Medical Imaging*, vol. 43, no. 12, pp. 4380–4392, 2024.
+> M. Sharifzadeh, S. Goudarzi, A. Tang, H. Benali, and H. Rivaz, "Mitigating aberration-induced noise: A deep learning-based aberration-to-aberration approach," *IEEE Transactions on Medical Imaging*, vol. 43, no. 12, pp. 4380–4392, 2024.
 
 ## Dataset Contributor(s)
 
-Mostafa Sharifzadeh (mostafa.sharifzadeh@mail.concordia.ca) and Hassan Rivaz
-(hassan.rivaz@concordia.ca), IMPACT Lab, Concordia University.
+- Mostafa Sharifzadeh <mostafa.sharifzadeh@mail.concordia.ca> (IMPACT Lab, Concordia University)
+- Hassan Rivaz <hassan.rivaz@concordia.ca> (IMPACT Lab, Concordia University)
 
 ## Dataset Creation Date
 
@@ -71,127 +42,68 @@ Mostafa Sharifzadeh (mostafa.sharifzadeh@mail.concordia.ca) and Hassan Rivaz
 
 ## License / Terms of Use
 
-CC BY 4.0.
-
-- **Simulated channel data, phantom parameters, and reconstruction/segmentation
-  labels** are generated by the contributors (Field II simulation outputs) and
-  released under CC BY 4.0.
-- **Segmentation mask shapes** (anechoic/hypoechoic/hyperechoic classes, 750
-  captures) are sourced from [Open Images V7](https://storage.googleapis.com/openimages/web/factsfigures_v7.html)'s
-  animal-class segmentation annotations (Google LLC). Only the binary mask is used — never the underlying source photograph. Open Images V7
-  licenses its annotations, including segmentation masks, under CC BY 4.0; the
-  source photographs themselves carry a separate CC BY 2.0 license but are not
-  used anywhere in this dataset.
-- **Diverse-echogenicity amplitude-weight maps** (1,000 captures) are photographs
-  from Wikimedia Commons, restricted to the `CC-BY-4.0` category and verified
-  per-image against each file's `CC BY 4.0` license field before acceptance.
-  `wikimedia_commons_metadata.csv`, at the dataset root, credits each one: a row
-  per capture, keyed by `DatasetFile` for `image_0751.hdf5` through
-  `image_1750.hdf5`, giving the source URL, Commons file page, author, title and
-  license. No photograph appears in the dataset in its original form. Each was
-  converted to grayscale, resampled to the 400 × 450 weight-map grid,
-  histogram-equalized and rescaled to `[0, 1]`. Values above 0.9 were then set to
-  1 and values below 0.1 to 0, producing the weight map stored as
-  `data/diverse_source_image`. This conversion is implemented in
-  `wikimedia_commons_postprocess.py`.
-- **Point-target phantoms** (250 captures) use no external asset — purely
-  synthetic point scatterers.
-
-All channel data was generated with Field II (Jensen/DTU), distributed for free academic use. Field II's terms do not
-restrict redistribution or licensing of simulation *output* data. Per Field II's terms, any use of this dataset should cite:
-J.A. Jensen, "Field: A Program for Simulating Ultrasound Systems," *Med. Biol.
-Eng. Comp.*, 1996; and J.A. Jensen and N.B. Svendsen, "Calculation of Pressure
-Fields from Arbitrarily Shaped, Apodized, and Excited Ultrasound Transducers,"
-*IEEE Trans. Ultrason., Ferroelec., Freq. Contr.*, 1992.
+[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode.en). Retain attribution and identify modifications when reusing the data.
 
 ## Intended Usage
 
-- **Generalized reconstruction** — learning a direct mapping from raw FSA channel
-  data to a B-mode image, and/or from FSA to arbitrary retrospective beamforming
-  targets (focused, multi-angle compounded, diverging-wave, adaptive).
-- **Compressed sensing / adaptive transmit** — sparse-aperture and adaptive transmit
-  design research using retrospective sub-selection of the 128 FSA transmit events.
-- **Segmentation** — pixel-aligned echogenicity-region segmentation (anechoic /
-  hypoechoic / hyperechoic) from raw channel data.
-- **Scatterer-level analysis** — `data/scatterers` exposes the exact Field II
-  point-scatterer cloud (position + amplitude) used to simulate each capture,
-  for tasks that want ground truth finer than a pixel grid (e.g. quantitative
-  ultrasound, scatterer-density estimation).
+- **Generalized reconstruction** — learning a direct mapping from raw FSA channel data to a B-mode image, and/or from FSA to arbitrary retrospective beamforming targets (focused, multi-angle compounded, diverging-wave, adaptive).
+- **Compressed sensing / adaptive transmit** — sparse-aperture and adaptive transmit design research using retrospective sub-selection of the 128 FSA transmit events.
+- **Segmentation** — pixel-aligned echogenicity-region segmentation (anechoic / hypoechoic / hyperechoic) from raw channel data.
+- **Scatterer-level analysis** — `data/scatterers` exposes the exact Field II point-scatterer cloud (position + amplitude) used to simulate each capture, for tasks that want ground truth finer than a pixel grid (e.g. quantitative ultrasound, scatterer-density estimation).
 
 ## Dataset Characterization
 
-- **Data Collection Method:** synthetic (Field II full-synthetic-aperture
-  simulation).
+- **Data Collection Method:** synthetic (Field II full-synthetic-aperture simulation).
 - **Labeling Method:** synthetic ground truth.
-  - Segmentation masks (anechoic/hypoechoic/hyperechoic) are Open Images V7
-    animal-class segmentation annotations, used for shape only.
-  - Diverse-echogenicity amplitude weighting uses a Wikimedia Commons photograph
-    as a continuous grayscale weight map, preserved as `data/diverse_source_image`
-    so an end user can see what pattern the capture was generated from.
-  - The reconstruction-target B-mode (`data/image`) is synthesized from the same
-    FSA capture (see *Data Validation* below), not an independent acquisition.
-- **Acquisition system (simulated):** 128-element linear array (model: L11-5v),
-  center frequency 5.208 MHz, element width 0.27 mm, kerf 0.03 mm, pitch 0.3 mm,
-  element height 5 mm, elevation focus (Rfocus) 20 mm. Simulated at 104.16 MHz (a
-  high rate required for Field II's numerical precision), then decimated ×5 to a
-  delivered sampling rate of 20.832 MHz. Sound speed 1540 m/s.
-  Receive dynamic-focus reconstruction uses F-number 1.75 (baked into
-  `reconstruct.py`'s `F_NUMBER` constant — zea's own default is 1.0, so this must be
-  supplied explicitly rather than relying on the file alone to reproduce the
-  reference images).
-- **Transmit pulse:** Hann-windowed 2.5-cycle tone burst at the 5.208 MHz center
-  frequency, sampled at the native 104.16 MHz simulation rate. −6dB fractional
-  bandwidth ≈ 76.5% (stored as `probe/probe_bandwidth_percent`).
-- **Coordinate convention:** x = lateral, y = elevation (always 0 for this 2D
-  acquisition), z = axial/depth — the standard zea convention, applied throughout
-  `probe_geometry`, `transmit_origins`, and every map's `coordinates` field.
-- **Frame timing:** each file is a single static frame (`n_frames=1`) with no
-  simulated motion or pulse-repetition-frequency concept, so `scan/time_to_next_transmit`
-  is intentionally omitted rather than populated with a fabricated value. Likewise, no
-  time-gain-compensation was applied to this synthetic data, so `scan/tgc_gain_curve`
-  is omitted.
+  - Segmentation masks (anechoic/hypoechoic/hyperechoic) are Open Images V7 animal-class segmentation annotations, used for shape only.
+  - Diverse-echogenicity amplitude weighting uses a Wikimedia Commons photograph as a continuous grayscale weight map, preserved as `data/diverse_source_image` so an end user can see what pattern the capture was generated from.
+  - The reconstruction-target B-mode (`data/image`) is synthesized from the same FSA capture (see *Data Validation* below), not an independent acquisition.
+- **Acquisition system (simulated):** 128-element linear array (model: L11-5v), center frequency 5.208 MHz, element width 0.27 mm, kerf 0.03 mm, pitch 0.3 mm, element height 5 mm, elevation focus (Rfocus) 20 mm. Simulated at 104.16 MHz (a high rate required for Field II's numerical precision), then decimated ×5 to a delivered sampling rate of 20.832 MHz. Sound speed 1540 m/s. Receive dynamic-focus reconstruction uses F-number 1.75 (baked into `reconstruct.py`'s `F_NUMBER` constant — zea's own default is 1.0, so this must be supplied explicitly rather than relying on the file alone to reproduce the reference images).
+- **Transmit pulse:** Hann-windowed 2.5-cycle tone burst at the 5.208 MHz center frequency, sampled at the native 104.16 MHz simulation rate. −6dB fractional bandwidth ≈ 76.5% (stored as `probe/probe_bandwidth_percent`).
+- **Coordinate convention:** x = lateral, y = elevation (always 0 for this 2D acquisition), z = axial/depth — the standard zea convention, applied throughout `probe_geometry`, `transmit_origins`, and every map's `coordinates` field.
+- **Frame timing:** each file is a single static frame (`n_frames=1`) with no simulated motion or pulse-repetition-frequency concept, so `scan/time_to_next_transmit` is intentionally omitted rather than populated with a fabricated value. Likewise, no time-gain-compensation was applied to this synthetic data, so `scan/tgc_gain_curve` is omitted.
+
+## Source Attribution
+
+The dataset as a whole is released under CC BY 4.0. Its components come from the following sources:
+
+- **Simulated channel data, phantom parameters, and reconstruction/segmentation labels** are generated by the contributors (Field II simulation outputs) and released under CC BY 4.0.
+- **Segmentation mask shapes** (anechoic/hypoechoic/hyperechoic classes, 750 captures) are sourced from [Open Images V7](https://storage.googleapis.com/openimages/web/factsfigures_v7.html)'s animal-class segmentation annotations (Google LLC). Only the binary mask is used — never the underlying source photograph. Open Images V7 licenses its annotations, including segmentation masks, under CC BY 4.0; the source photographs themselves carry a separate CC BY 2.0 license but are not used anywhere in this dataset.
+- **Diverse-echogenicity amplitude-weight maps** (1,000 captures) are photographs from Wikimedia Commons, restricted to the `CC-BY-4.0` category and verified per-image against each file's `CC BY 4.0` license field before acceptance. `wikimedia_commons_metadata.csv`, at the dataset root, credits each one: a row per capture, keyed by `DatasetFile` for `image_0751.hdf5` through `image_1750.hdf5`, giving the source URL, Commons file page, author, title and license. No photograph appears in the dataset in its original form. Each was converted to grayscale, resampled to the 400 × 450 weight-map grid, histogram-equalized and rescaled to `[0, 1]`. Values above 0.9 were then set to 1 and values below 0.1 to 0, producing the weight map stored as `data/diverse_source_image`. This conversion is implemented in `wikimedia_commons_postprocess.py`.
+- **Point-target phantoms** (250 captures) use no external asset — purely synthetic point scatterers.
+
+All channel data was generated with Field II (Jensen/DTU), distributed for free academic use. Field II's terms do not restrict redistribution or licensing of simulation *output* data. Per Field II's terms, any use of this dataset should cite: J.A. Jensen, "Field: A Program for Simulating Ultrasound Systems," *Med. Biol. Eng. Comp.*, 1996; and J.A. Jensen and N.B. Svendsen, "Calculation of Pressure Fields from Arbitrarily Shaped, Apodized, and Excited Ultrasound Transducers," *IEEE Trans. Ultrason., Ferroelec., Freq. Contr.*, 1992.
+
+## Processing the Dataset
+
+The acquisitions can be processed with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea).
+
+`zea` streams the data from the Hugging Face Hub and processes it according to the pipeline. You can try it out with the following command:
+
+```bash
+zea process \
+  --dataset hf://nvidia/OpenH-RF/concordia/data/image_0005.hdf5 \
+  --config hf://nvidia/OpenH-RF/concordia/pipeline.yaml \
+  --n-frames 1 \
+  --save-as png
+```
+
+Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/concordia/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF).
 
 ## Dataset Format
 
-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`, `pipeline.yaml`, and this card sit
-at the repository root. Per file:
-
-- `data/raw_data` — full FSA channel data, `int16`, quantized from the native
-  simulated (float32) values by per-file peak-scaling into the int16 range
-  (~5e-5 relative quantization step — well below any physically meaningful signal
-  feature, equivalent to how real ultrasound hardware ADCs store raw channel data).
-- `data/image` — the reference B-mode: a synthetic transmit aperture (STA)
-  reconstruction that delay-and-sums all 128 single-element FSA transmits with
-  zea's default DAS pipeline over the intended imaging FOV (see *Subject
-  Metadata*). This is exactly what `reconstruct.py` reproduces from `data/raw_data`.
-- `data/segmentation` — boolean mask with labels `["background", <class>]`,
-  present only for anechoic/hypoechoic/hyperechoic phantoms (750 captures) —
-  the Open Images V7 mask directly. Not produced for diverse or point-target
-  phantoms (no defined region to segment in either case).
-- `data/echogenicity_multiplier` — present alongside `data/segmentation`
-  (same 750 captures): the scalar amplitude multiplier Field II applied to
-  every scatterer inside that region, e.g. "hypoechoic at 0.23×," not just
-  "hypoechoic." A single number per file, so it's stored without a
-  `coordinates` grid (optional per zea's `Map` spec — this isn't spatial).
-- `data/diverse_source_image` — present only for diverse-class phantoms
-  (1,000 captures): the grayscale natural-image echogenicity weight map
-  (continuous `[0, 1]` values) each capture's scatterer amplitudes were
-  generated from. Not a mask — just the reference image, so an end user can
-  see what pattern produced the capture.
-- `data/scatterers` — the exact Field II point-scatterer cloud used to
-  simulate the capture (every capture, all classes): `values` = per-scatterer
-  amplitude (a.u.), `coordinates` = per-scatterer `(x, y, z)` position in
-  metres. ~275,000 scatterers per capture, stored via zea's custom-data
-  extension mechanism as a `(1, n_scatterers, 1)` / `(n_scatterers, 1, 3)`
-  pair rather than a regular pixel grid (a point cloud has no grid to speak
-  of). This is the ground truth Field II actually simulated from — finer than
-  any pixel-grid label derived from it.
-
-Pre-processing applied before packaging: anti-alias FIR decimation (factor 5,
-104.16 MHz → 20.832 MHz) and int16 quantization, both described above. zea's
-The original submission used lossless `lzf` HDF5 compression; re-saving may change storage compression without changing the RF representation.
+[zea v0.1.6](https://github.com/tue-bmd/zea)
+
+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`, `pipeline.yaml`, and this card sit at the repository root. Per file:
+
+- `data/raw_data` — full FSA channel data, `int16`, quantized from the native simulated (float32) values by per-file peak-scaling into the int16 range (~5e-5 relative quantization step — well below any physically meaningful signal feature, equivalent to how real ultrasound hardware ADCs store raw channel data).
+- `data/image` — the reference B-mode: a synthetic transmit aperture (STA) reconstruction that delay-and-sums all 128 single-element FSA transmits with zea's default DAS pipeline over the intended imaging FOV (see *Subject Metadata*). This is exactly what `reconstruct.py` reproduces from `data/raw_data`.
+- `data/segmentation` — boolean mask with labels `["background", <class>]`, present only for anechoic/hypoechoic/hyperechoic phantoms (750 captures) — the Open Images V7 mask directly. Not produced for diverse or point-target phantoms (no defined region to segment in either case).
+- `data/echogenicity_multiplier` — present alongside `data/segmentation` (same 750 captures): the scalar amplitude multiplier Field II applied to every scatterer inside that region, e.g. "hypoechoic at 0.23×," not just "hypoechoic." A single number per file, so it's stored without a `coordinates` grid (optional per zea's `Map` spec — this isn't spatial).
+- `data/diverse_source_image` — present only for diverse-class phantoms (1,000 captures): the grayscale natural-image echogenicity weight map (continuous `[0, 1]` values) each capture's scatterer amplitudes were generated from. Not a mask — just the reference image, so an end user can see what pattern produced the capture.
+- `data/scatterers` — the exact Field II point-scatterer cloud used to simulate the capture (every capture, all classes): `values` = per-scatterer amplitude (a.u.), `coordinates` = per-scatterer `(x, y, z)` position in metres. ~275,000 scatterers per capture, stored via zea's custom-data extension mechanism as a `(1, n_scatterers, 1)` / `(n_scatterers, 1, 3)` pair rather than a regular pixel grid (a point cloud has no grid to speak of). This is the ground truth Field II actually simulated from — finer than any pixel-grid label derived from it.
+
+Pre-processing applied before packaging: anti-alias FIR decimation (factor 5, 104.16 MHz → 20.832 MHz) and int16 quantization, both described above. zea's The original submission used lossless `lzf` HDF5 compression; re-saving may change storage compression without changing the RF representation.
 
 ## Dataset Quantification
 
@@ -207,16 +119,12 @@ This dataset comprises 2,000 FSA captures across five echogenicity classes:
 | Diverse (natural-image-derived) | 1,000 | 0751–1750 | No | `data/diverse_source_image` |
 | Point-target | 250 | 1751–2000 | No | — |
 
-`data/scatterers` (the raw point-scatterer cloud) is present for all 2,000
-captures regardless of class.
+`data/scatterers` (the raw point-scatterer cloud) is present for all 2,000 captures regardless of class.
 
-Each capture's class label is stored in its file metadata
-(`metadata/annotations/label`).
+Each capture's class label is stored in its file metadata (`metadata/annotations/label`).
 
 - **Stored HDF5 size:** 82.39 GB total; 41.20 MB per file on average. RF remains int16 and post-decimation.
-- **Train / validation / test split:** none predefined — the corpus is released
-  as a single set for users to partition as their task requires (class membership
-  and index ranges are given above).
+- **Train / validation / test split:** none predefined — the corpus is released as a single set for users to partition as their task requires (class membership and index ranges are given above).
 - **Per-sample feature table:**
 
 | Field | Shape | Dtype | Units | Description |
@@ -238,72 +146,24 @@ Each capture's class label is stored in its file metadata
 
 ## Subject Metadata
 
-Not applicable — no human or animal subjects. Each "subject" is a simulated
-phantom of ≈275,000 scatterers. The reconstructed image FOV is 45 mm (lateral) ×
-40 mm (axial), starting 10 mm from the transducer face. The scatterer field is
-deliberately larger than this FOV (≈49.5 mm wide, extending to 54 mm deep) so
-that beamforming at the FOV edges is fully supported by surrounding scatterers
-and free of edge-truncation artifacts; only the reconstruction grid is cropped
-to the FOV, while `data/raw_data` and `data/scatterers` retain the full extent.
-Per-class amplitude weighting inside the mask/weight-map region:
+Not applicable — no human or animal subjects. Each "subject" is a simulated phantom of ≈275,000 scatterers. The reconstructed image FOV is 45 mm (lateral) × 40 mm (axial), starting 10 mm from the transducer face. The scatterer field is deliberately larger than this FOV (≈49.5 mm wide, extending to 54 mm deep) so that beamforming at the FOV edges is fully supported by surrounding scatterers and free of edge-truncation artifacts; only the reconstruction grid is cropped to the FOV, while `data/raw_data` and `data/scatterers` retain the full extent. Per-class amplitude weighting inside the mask/weight-map region:
 
 - **Anechoic:** scatterer amplitude zeroed.
-- **Hypoechoic:** amplitude × U[0.07, 0.50]; the exact per-capture draw is
-  preserved in `data/echogenicity_multiplier`.
-- **Hyperechoic:** amplitude × U[2, 8]; same per-capture preservation via
-  `data/echogenicity_multiplier`.
-- **Diverse:** continuous grayscale weight map from a Wikimedia Commons
-  photograph (histogram-equalized, normalized to [0, 1], clipped at the
-  extremes) — preserved as `data/diverse_source_image`.
+- **Hypoechoic:** amplitude × U[0.07, 0.50]; the exact per-capture draw is preserved in `data/echogenicity_multiplier`.
+- **Hyperechoic:** amplitude × U[2, 8]; same per-capture preservation via `data/echogenicity_multiplier`.
+- **Diverse:** continuous grayscale weight map from a Wikimedia Commons photograph (histogram-equalized, normalized to [0, 1], clipped at the extremes) — preserved as `data/diverse_source_image`.
 - **Point-target:** 10–20 point targets per phantom (count ~ U[10, 20], rounded to an integer), each target's amplitude an independent U[15, 35] draw.  The targets consist of adjacent groups of high-amplitude scatterers within `data/scatterers`.
 
-All classes except point-target additionally include 2–5 extra bright point
-scatterers (amplitude 18–22) scattered at valid random positions, for realism.
+All classes except point-target additionally include 2–5 extra bright point scatterers (amplitude 18–22) scattered at valid random positions, for realism.
 
 ## Data Validation
 
-**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 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. 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|, all on shared equal-aspect mm
-axes, confirming the label, reconstruction, and scatterer field are spatially
-registered. [`assets/reference_capture.png`](assets/reference_capture.png) is one such figure.
+`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. 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|, all on shared equal-aspect mm axes, confirming the label, reconstruction, and scatterer field are spatially registered. [`assets/reference_capture.png`](assets/reference_capture.png) is one such figure.
 
 ## Known Issues
 
-- **`data/image` is a log-compressed dB B-mode, not raw beamformed RF** —
-  This is a minor packaging note, not a coverage
-  gap, precisely because the data is FSA: `data/raw_data` retains every
-  element-to-element transmit/receive combination (128 single-element transmit
-  firings × 128-element receive aperture on the L11-5v), which is a more
-  general representation than any one fixed beamformed product could be: by
-  delay-and-sum with the appropriate per-element transmit delays and apodization
-  (linear superposition over the 128 single-element firings), it is a sufficient
-  basis to retrospectively synthesize other transmit/receive schemes — multi-angle
-  plane-wave compounding at arbitrary steering angles, diverging-wave imaging from
-  an arbitrary virtual source behind the array, conventional focused/multi-line
-  transmit — with no re-acquisition. `reconstruct.py` as shipped only implements
-  one of these (the full 128-transmit STA beamforming used to produce
-  `data/image`, via zea's default DAS pipeline, `pipeline.yaml`); it does not take
-  a scheme argument. Reconstructing a different scheme means modifying
-  `reconstruct.py` accordingly — supplying the corresponding transmit
-  delays/apodization to zea's `Beamform` op.
+- **`data/image` is a log-compressed dB B-mode, not raw beamformed RF** — This is a minor packaging note, not a coverage gap, precisely because the data is FSA: `data/raw_data` retains every element-to-element transmit/receive combination (128 single-element transmit firings × 128-element receive aperture on the L11-5v), which is a more general representation than any one fixed beamformed product could be: by delay-and-sum with the appropriate per-element transmit delays and apodization (linear superposition over the 128 single-element firings), it is a sufficient basis to retrospectively synthesize other transmit/receive schemes — multi-angle plane-wave compounding at arbitrary steering angles, diverging-wave imaging from an arbitrary virtual source behind the array, conventional focused/multi-line transmit — with no re-acquisition. `reconstruct.py` as shipped only implements one of these (the full 128-transmit STA beamforming used to produce `data/image`, via zea's default DAS pipeline, `pipeline.yaml`); it does not take a scheme argument. Reconstructing a different scheme means modifying `reconstruct.py` accordingly — supplying the corresponding transmit delays/apodization to zea's `Beamform` op.
 
 ## Ethical Considerations
 
-Entirely synthetic data. No human or animal subjects are involved, and no IRB/ethics
-approval is required or applicable. The "animal-class" Open Images V7 segmentation
-annotations and Wikimedia Commons photographs referenced elsewhere in this card
-contribute only geometric silhouette shapes and grayscale texture patterns,
-respectively — no live animal or human subject, tissue, or imagery of either is
-used anywhere in this dataset.
+Entirely synthetic data. No human or animal subjects are involved, and no IRB/ethics approval is required or applicable. The "animal-class" Open Images V7 segmentation annotations and Wikimedia Commons photographs referenced elsewhere in this card contribute only geometric silhouette shapes and grayscale texture patterns, respectively — no live animal or human subject, tissue, or imagery of either is used anywhere in this dataset.