siemens-healthineers: restructure the data card to the common layout

siemens-healthineers/README.md

@@ -1,5 +1,6 @@
 ---
-pretty_name: "OpenH-RF — Siemens In-Vivo Raw Ultrasound Channel Data"
+name: siemens-healthineers
+pretty_name: "Siemens In-Vivo Raw Ultrasound Channel Data"
 license: cc-by-4.0
 task_categories:
   - other
@@ -16,47 +17,23 @@ size_categories:
   - 1K<n<10K
 ---
 
-# Data Card — Siemens In-vivo Raw Ultrasound Channel Data
+# Siemens In-vivo Raw Ultrasound Channel Data
 
 ![DAS B-mode reconstructions of four anatomies from the Siemens FSA channel data](assets/anatomies.png)
 
-Four of the anatomies in this dataset, each beamformed from `data/raw_data`:
-`Subject_03_acq_018`, `Subject_02_acq_004`, `Subject_03_acq_008`,
-`Subject_05_acq_031`.
-
-`zea` renders any acquisition straight from the Hub with the
-`pipeline.yaml` in this folder. Try it out with the following command:
-
-```bash
-zea process \
-  --dataset hf://nvidia/OpenH-RF/siemens-healthineers/data/Subject_03_acq_008.hdf5 \
-  --config hf://nvidia/OpenH-RF/siemens-healthineers/pipeline.yaml
-```
+*Four of the anatomies in this dataset, each beamformed from `data/raw_data`: [`Subject_03_acq_018`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/siemens-healthineers/data/Subject_03_acq_018.hdf5), [`Subject_02_acq_004`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/siemens-healthineers/data/Subject_02_acq_004.hdf5), [`Subject_03_acq_008`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/siemens-healthineers/data/Subject_03_acq_008.hdf5), [`Subject_05_acq_031`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/siemens-healthineers/data/Subject_05_acq_031.hdf5).*
 
 ## Dataset Description
 
-This is an in-vivo full-synthetic-aperture (FSA) ultrasound raw channel-data
-dataset for research on sound speed estimation, phase aberration correction,
-and learned beamforming from pre-beamformed data. Each acquisition is a
-multi-frame FSA capture from a 180-element linear array: raw baseband IQ
-channel data alongside reference B-mode images. The dataset is a collection of volunteer scans including 15 adult subjects, 590
-acquisitions of predominantly superficial anatomy acquired on a Siemens
-ACUSON Sequoia development scanner. **Data type: in-vivo human.**
+This is an in-vivo full-synthetic-aperture (FSA) ultrasound raw channel-data dataset for research on sound speed estimation, phase aberration correction, and learned beamforming from pre-beamformed data. Each acquisition is a multi-frame FSA capture from a 180-element linear array: raw baseband IQ channel data alongside reference B-mode images. The dataset is a collection of volunteer scans including 15 adult subjects, 590 acquisitions of predominantly superficial anatomy acquired on a Siemens ACUSON Sequoia development scanner. **Data type: in-vivo human.**
 
 ## Dataset Contributor(s)
 
-Siemens Healthineers, Ultrasound. Contributors:
-
-- Rickard Loftman, PhD
-- Ismayil Guracar, MSEE
-- Jane Bucholz, BHS (Medical Imaging); PgDip HS (Ultrasound); DMU (General); PgDip Public Health (Dist)
-- Craig Williams, PG Dip Medical Ultrasound, BSc (Hons) Diagnostic Radiography
-
-Additional contributor:
-
-- Walter Simson, PhD
-
-Contact: Ismayil Guracar ([ismayil.guracar@siemens-healthineers.com](mailto:ismayil.guracar@siemens-healthineers.com)).
+- Rickard Loftman, PhD (Siemens Healthineers, Ultrasound)
+- Ismayil Guracar, MSEE <ismayil.guracar@siemens-healthineers.com> (Siemens Healthineers, Ultrasound; contact)
+- Jane Bucholz, BHS (Medical Imaging); PgDip HS (Ultrasound); DMU (General); PgDip Public Health (Dist) (Siemens Healthineers, Ultrasound)
+- Craig Williams, PG Dip Medical Ultrasound, BSc (Hons) Diagnostic Radiography (Siemens Healthineers, Ultrasound)
+- Walter Simson, PhD (additional contributor)
 
 ## Dataset Creation Date
 
@@ -64,38 +41,41 @@ Contact: Ismayil Guracar ([ismayil.guracar@siemens-healthineers.com](mailto:isma
 
 ## License / Terms of Use
 
-[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode.en). 
+[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
 
-Developing and benchmarking methods that operate on raw ultrasound channel
-data: learned/adaptive beamforming, and self-supervised representation
-learning on RF/IQ data. Subject IDs enable subject-wise splits. the 
-reference split holds out Subject_13 and Subject_14 for validation.
+Developing and benchmarking methods that operate on raw ultrasound channel data: learned/adaptive beamforming, and self-supervised representation learning on RF/IQ data. Subject IDs enable subject-wise splits. the reference split holds out Subject_13 and Subject_14 for validation.
 
 ## Dataset Characterization
 
-- **Data Collection Method:** healthy adult volunteers in a
-non-clinical research setting.
-- **Labeling Method:** human-annotated subject/anatomy metadata and
-per-frame quality tags (`contact`, `challenging`). 
-- **Acquisition system:** Siemens ACUSON Sequoia development scanner, 10L4
-180-element linear array (0.201 mm pitch, 36.2 mm aperture; elements
-0.2 × 9 mm); transmit center frequency 6.5 MHz; baseband IQ sampled at
-13.33 MHz. Full synthetic aperture multi-static acquisition: 180
-single-element transmit events per frame, all 180 elements received.
+- **Data Collection Method:** healthy adult volunteers in a non-clinical research setting.
+- **Labeling Method:** human-annotated subject/anatomy metadata and per-frame quality tags (`contact`, `challenging`).
+- **Acquisition system:** Siemens ACUSON Sequoia development scanner, 10L4 180-element linear array (0.201 mm pitch, 36.2 mm aperture; elements 0.2 × 9 mm); transmit center frequency 6.5 MHz; baseband IQ sampled at 13.33 MHz. Full synthetic aperture multi-static acquisition: 180 single-element transmit events per frame, all 180 elements received.
+
+## 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/siemens-healthineers/data/Subject_03_acq_008.hdf5 \
+  --config hf://nvidia/OpenH-RF/siemens-healthineers/pipeline.yaml
+```
+
+Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/siemens-healthineers/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF).
 
 ## Dataset Format
 
-Packaged in the `zea` [file format](https://zea.readthedocs.io/en/openh-rf-latest/data-acquisition.html)
-(current release `zea_version` 0.1.6), **one HDF5
-file per acquisition**:
+[zea v0.1.6](https://github.com/tue-bmd/zea)
 
-- On-scanner quadrature demodulation to baseband IQ, demodulation frequency stored per
-file.
+Packaged in the `zea` [file format](https://zea.readthedocs.io/en/openh-rf-latest/data-acquisition.html) (current release `zea_version` 0.1.6), **one HDF5 file per acquisition**:
+
+- On-scanner quadrature demodulation to baseband IQ, demodulation frequency stored per file.
 - On-scanner analog TGC and digital filtering with scaling are applied.
-- FSA transmit scheme encoded explicitly (one-hot `tx_apodizations`,
-zero `t0_delays`, transmit origins at element positions).
+- FSA transmit scheme encoded explicitly (one-hot `tx_apodizations`, zero `t0_delays`, transmit origins at element positions).
 
 Per-file contents:
 
@@ -127,60 +107,33 @@ All spatial maps carry per-pixel Cartesian `coordinates` in meters, last axis `[
 
 **Current OpenH-RF release:** 590 HDF5 files; 228.54 GB (228,538,449,920 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
 
-- **590 acquisitions (4,539 frames) from 15 subjects**, one continuous
-dataset: 51 acquisitions / 411 frames (Subjects 01–03) from the first
-acquisition round and 539 acquisitions / 4,128 frames (Subjects 04–15) from
-the second.
-- **File naming:** one HDF5 per acquisition, `Subject_XX_acq_NNN.hdf5`
-(acquisitions numbered per subject in acquisition order).
+- **590 acquisitions (4,539 frames) from 15 subjects**, one continuous dataset: 51 acquisitions / 411 frames (Subjects 01–03) from the first acquisition round and 539 acquisitions / 4,128 frames (Subjects 04–15) from the second.
+- **File naming:** one HDF5 per acquisition, `Subject_XX_acq_NNN.hdf5` (acquisitions numbered per subject in acquisition order).
 - **Stored HDF5 size:** 228.54 GB (228,538,449,920 bytes); raw IQ stored as float32 I/Q, one HDF5 per acquisition.
-- Per-frame boolean quality labels support curation: `contact` and
-`challenging` (see feature table). Acquisitions not yet through
-manual review default to `contact=True, challenging=False`.
+- Per-frame boolean quality labels support curation: `contact` and `challenging` (see feature table). Acquisitions not yet through manual review default to `contact=True, challenging=False`.
 
 ## Subject Metadata
 
 Aggregate statistics only; files carry anonymized subject IDs, no PHI.
 
 - **15 adult volunteers in total** (Subject_01–Subject_15).
-- Acquisitions per subject (590 total): Subject_01: 12, Subject_02: 6,
-Subject_03: 33, Subject_04: 44, Subject_05: 41, Subject_06: 47,
-Subject_07: 35, Subject_08: 51, Subject_09: 44, Subject_10: 32,
-Subject_11: 49, Subject_12: 36, Subject_13: 36, Subject_14: 52,
-Subject_15: 72.
-- Anatomies scanned (by acquisition): breast (284), thyroid (117),
-axilla (59), neck (29), abdomen (20), carotid (18), other/unlabeled (63).
-- All scans are B-mode-style views (predominantly superficial anatomy) on a
-single probe and system; no pathology labels are asserted.
+- Acquisitions per subject (590 total): Subject_01: 12, Subject_02: 6, Subject_03: 33, Subject_04: 44, Subject_05: 41, Subject_06: 47, Subject_07: 35, Subject_08: 51, Subject_09: 44, Subject_10: 32, Subject_11: 49, Subject_12: 36, Subject_13: 36, Subject_14: 52, Subject_15: 72.
+- Anatomies scanned (by acquisition): breast (284), thyroid (117), axilla (59), neck (29), abdomen (20), carotid (18), other/unlabeled (63).
+- All scans are B-mode-style views (predominantly superficial anatomy) on a single probe and system; no pathology labels are asserted.
 
 ## Data Validation
 
-[`pipeline.yaml`](pipeline.yaml). `data/image` is produced by this same
-pipeline at conversion time, so `reconstruct.py` reproduces it — a round-trip
-check that the probe geometry, transmit description, and timing are recorded
-correctly. Reference B-modes rendered by this pipeline are in
-[`assets/`](assets), and the strip at the top of this card is four of them.
+[`pipeline.yaml`](pipeline.yaml). `data/image` is produced by this same pipeline at conversion time, so `reconstruct.py` reproduces it — a round-trip check that the probe geometry, transmit description, and timing are recorded correctly. Reference B-modes rendered by this pipeline are in [`assets/`](assets), and the strip at the top of this card is four of them.
 
 ![Carotid cineloop, ten frames](assets/Subject_03_acq_008.gif)
 
-Ten frames of `data/Subject_03_acq_008.hdf5`. `custom/contact` marks the first
-frame as off-subject — use those per-frame flags when picking frames, rather
-than assuming frame 0 is usable.
-
+Ten frames of `data/Subject_03_acq_008.hdf5`. `custom/contact` marks the first frame as off-subject — use those per-frame flags when picking frames, rather than assuming frame 0 is usable.
 
 ## Known Issues
 
-- Time-axis length (`n_ax` = `raw_data.shape[2]`) varies per acquisition
-(native lengths range ~700–2270 samples); each file is stored at its own
-native length.
+- Time-axis length (`n_ax` = `raw_data.shape[2]`) varies per acquisition (native lengths range ~700–2270 samples); each file is stored at its own native length.
 - `view_difficulty` annotations exist for the first acquisition round only.
 
 ## Ethical Considerations
 
-The released data is anonymized and contains no protected health information
-(PHI). Acquisitions were performed under Siemens Healthineers internal
-ultrasound research safety, human-safety, and consent procedures. Metadata
-labels, file names, and source identifiers are screened against the HIPAA Safe
-Harbor standard, with GDPR-equivalent handling for any EU-origin data; this
-release contains no EU-origin data. The dataset is released under the CC BY
-4.0 license.
+The released data is anonymized and contains no protected health information (PHI). Acquisitions were performed under Siemens Healthineers internal ultrasound research safety, human-safety, and consent procedures. Metadata labels, file names, and source identifiers are screened against the HIPAA Safe Harbor standard, with GDPR-equivalent handling for any EU-origin data; this release contains no EU-origin data. The dataset is released under the CC BY 4.0 license.