politorino: sync data card and figures with GitHub

politorino/README.md

@@ -1,4 +1,5 @@
 ---
+name: politorino
 pretty_name: High Frame Rate Fascicle Tracking - PoliTO
 license: cc-by-4.0
 task_categories:
@@ -16,15 +17,30 @@ size_categories:
   - n<1K
 ---
 
-# Data Card — High Frame Rate Fascicle Tracking - PoliTO - Torino, Italy
+# PoliTO High Frame Rate Fascicle Tracking
+
+![Reconstructed cineloop from PAT02_w1.hdf5](assets/PAT02_w1.gif)
+
+*Cine loop of treadmill walking at 2 km/h, [`data/PAT02/PAT02_w1.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/politorino/data/PAT02/PAT02_w1.hdf5), reconstructed from the raw channel data with the `pipeline.yaml` in this folder.*
 
 ## Dataset Description
 
 The dataset comprises in-vivo human musculoskeletal raw ultrasound data during dynamic tasks. It includes the acquisition of the medial gastrocnemius on 5 healthy volunteers  during heel raises and treadmill walking. The data is acquired with a Verasonics Vantage 256 research platform and a 128-element linear array probe (L11-5v) at 500 fps for 9.6 s per acquisition. Each subject was imaged during six task conditions: cyclical heel raises and drops at a fixed frequency provided by a metronome at 60 (hr1), 90 (hr2), and 120 bpm (hr3) and walking at 2 (w1), 4 (w2), and 5 (w3) km/h. We provide the raw data, the beamformed DAS and FDMAS images, and the automated fascicle tracking obtained with UltraTimTrack (https://github.com/timvanderzee/UltraTimTrack). The tracking data was obtained on the first 9s of sub-sampled .mp4 videos at three frame rates: 25 fps, 50 fps and 125 fps. The tracking data is provided for each frame rate and a Python code is provided to correctly visualize the data on the reconstructed images.  **Data type: in-vivo**
 
-## Dataset Contributors
+## Dataset Contributor(s)
+
+Personnel involved in raw data acquisition, beamforming, annotation, dataset preparation and analysis:
 
-Contributors include the personnel involved in raw data acquisition, beamforming, annotation, dataset preparation and analysis: E. Cesti, M. Carbonaro, M. Boccardo, F. Truscello, S. Seoni, G.L. Cerone, K.M. Meiburger, G. Bardoscia, B.J. Raiteri, and A. Botter. 
+- E. Cesti
+- M. Carbonaro
+- M. Boccardo
+- F. Truscello
+- S. Seoni
+- G.L. Cerone
+- K.M. Meiburger
+- G. Bardoscia
+- B.J. Raiteri
+- A. Botter
 
 ## Dataset Creation Date
 
@@ -32,8 +48,7 @@ Contributors include the personnel involved in raw data acquisition, beamforming
 
 ## License / Terms of Use
 
-[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode.en).
-The data is cleared for this license (see ethical considerations below).
+[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
 
@@ -43,21 +58,24 @@ This data was employed for an initial study evaluating fascicle tracking algorit
 
 - **Data Collection Method:** in-vivo human musculoskeletal
 - **Labeling Method:** derived fascicle tracking from UltraTimTrack
-- **Acquisition system:** 128-element linear array (L11-5v),
-  sampling 31.25 MHz, center frequency 7.6 MHz
+- **Acquisition system:** 128-element linear array (L11-5v), sampling 31.25 MHz, center frequency 7.6 MHz
 
-## Dataset Format
+## Processing the Dataset
+
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/politorino/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
 
-Submitted in the [`zea` file format](https://zea.readthedocs.io/en/latest/)
-(one HDF5 file per acquisition). 
-`reconstruct.py` reconstructs a B-mode from the raw channel data and overlays
-the stored fascicle tracking on it, writing a `.png`. An example output is
-provided. The constants at the top of the script select what is drawn:
+The script reconstructs a B-mode from the raw channel data and overlays the stored fascicle tracking on it, writing a `.png`. The constants at the top of the script select what is drawn:
 
 - `FRAME` -- index of the acquisition frame to reconstruct
 - `FPS` -- which stored tracking rate to overlay (25, 50 or 125 fps)
 - `TRACK_INDEX` -- index of the tracking sample within that rate
 
+## Dataset Format
+
+[zea v0.1.6](https://github.com/tue-bmd/zea)
+
+Submitted in the [`zea` file format](https://zea.readthedocs.io/en/latest/) (one HDF5 file per acquisition).
+
 Per-sample contents of the HDF5:
 
 | Group / field | Shape | Dtype | Units | Description |
@@ -69,7 +87,6 @@ Per-sample contents of the HDF5:
 | `probe` | -- | -- | -- | element width, probe_center_frequency, probe_geometry |
 | `metadata/tracking_` | -- | -- | -- | Tracking data as saved by UltraTimTrack |
 
-
 ## Dataset Quantification
 
 **Current OpenH-RF release:** 30 HDF5 files; 169.95 GB (169,950,314,496 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
@@ -79,25 +96,21 @@ Per-sample contents of the HDF5:
 
 ## Subject Metadata
 
-Five healthy subjects (2 men, 3 women; (mean ± SD) age: 25.6 ± 1.3 yr, height:  1.78 ± 0.06 m, weight: 67 ± 11 kg) were recruited. All data were acquired with the same Verasonics system and probe. 
+Five healthy subjects (2 men, 3 women; (mean ± SD) age: 25.6 ± 1.3 yr, height:  1.78 ± 0.06 m, weight: 67 ± 11 kg) were recruited. All data were acquired with the same Verasonics system and probe.
 
 ## Data Validation
 
-[`reconstruct.py`](reconstruct.py) builds a `zea.Pipeline` of DAS beamforming →
-envelope detection → normalization → log-compression **in code** and reconstructs
-a B-mode directly from `raw_data` — showing the raw-to-image flow without any
-config file. It contains also the code employed to view the plotting of the tracking data. It also saves the pipeline to [`pipeline.yaml`](pipeline.yaml) as a
-shareable recipe. 
+`reconstruct.py` builds a `zea.Pipeline` of DAS beamforming → envelope detection → normalization → log-compression **in code** and reconstructs a B-mode directly from `raw_data` — showing the raw-to-image flow without any config file. It contains also the code employed to view the plotting of the tracking data. It also saves the pipeline to `pipeline.yaml` as a shareable recipe.
 
 ## Known Issues
 
-- The tracking data for three subjects (PAT01, PAT04, PAT05) were obtained on the reconstructed images after a horizontal flip. This is noted in the file and the provided code to view the tracking data automatically checks for this and flips the data, if needed. 
+- The tracking data for three subjects (PAT01, PAT04, PAT05) were obtained on the reconstructed images after a horizontal flip. This is noted in the file and the provided code to view the tracking data automatically checks for this and flips the data, if needed.
 - The tracking data covers the first 9s out of the provided 9.6s of the raw data.
 - There is a small offset in depth between the reconstructed data used for the tracking and the raw data. The `reconstruct.py` code provides the correction at lines 233-240.
 
 ## Ethical Considerations
 
-The study was conducted in accordance with the Declaration of Helsinki and the procedure approved by the Institutional Ethics Committee of Politecnico di Torino (reference number: 2772/2025). Informed consent was obtained from all participants after receiving detailed explanation of the study procedures and before participating in the study. 
+The study was conducted in accordance with the Declaration of Helsinki and the procedure approved by the Institutional Ethics Committee of Politecnico di Torino (reference number: 2772/2025). Informed consent was obtained from all participants after receiving detailed explanation of the study procedures and before participating in the study.
 
 ## Citation