Add waterloo-largeartery README + pipeline(s) from accepted_submissions

waterloo-largeartery/README.md

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+---
+pretty_name: UW-FemVeinRF
+license: cc-by-4.0
+task_categories:
+  - other
+tags:
+  - ultrasound
+  - rf
+  - openh-rf
+  - medical-imaging
+  - in-vivo
+  - femoral-vein
+  - beamforming
+  - vector-doppler
+  - dealiasing
+  - vector-flow-imaging
+language:
+  - en
+size_categories:
+  - n<500K
+---
+
+# UW-FemVein RF
+
+Dataset consisting of raw RF data and vector velocity measurements of femoral vein acquired in studies conducted by VORTEX @
+University of Waterloo.
+
+## Dataset Description
+
+This is a dataset consisting of raw RF frames (plane wave) and vector flow
+profiles of the femoral vein in humans, acquired using a programmable research scanner configured for
+high frame rate vector flow imaging. The data was collected as part of studies
+conducted by the VORTEX research group at the University of Waterloo, focusing on
+venous hemodynamics under muscular contraction and head up tilt.
+
+## Dataset Contributor(s)
+
+Hassan Nahas, Jeremy N. Cohen, Eudoxia Zafiris, Skye H.T. Ling, Alfred C.H. Yu, Jason S. Au
+
+Correspondence emails:
+hassan.nahas@uwaterloo.ca
+jason.au@uwaterloo.ca
+
+
+## Dataset Creation Date
+
+08/20/2026
+
+## License / Terms of Use
+
+[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode.en).
+
+All human studies were approved by the University of Waterloo’s Human Research
+Ethics Board (ORE #46018). All included data was acquired from participants who
+provided both written and verbal consent prior to participating in the study
+regarding public data sharing.
+
+## Intended Usage
+
+Developing, benchmarking, and evaluating methods for ultrasound image
+reconstruction, motion estimation, clutter filtering, multi-angle Doppler
+processing, and vector flow imaging (VFI) in venous imaging.
+
+## Dataset Characterization
+
+- **Data Collection Method:** In vivo imaging of human femoral veins.
+- **Labeling Method:** Label contains target vessel (Anatomy), view direction (Longitudinal), and physiological condition (Contraction + head up tilt).
+- **Acquisition system:**
+  Raw RF data was acquired from programmable research scanners (US4R, US4US, Warsaw, Poland) equipped with an AL2442 linear array transducer.
+
+## Dataset Format
+
+Submitted in the [`zea` file format](https://zea.readthedocs.io/en/v0.1.0a3/data-acquisition.html) (one HDF5 file per acquisition).
+
+Per-sample contents of the converted HDF5:
+
+| Group / field | Shape | Dtype | Units | Description |
+|---|---|---|---|---|
+| `metadata/credit` | `[]` | str | -- | Dataset attribution (LITMUS @ University of Waterloo) |
+| `metadata/subject/id` | `[]` | str | -- | Participant ID (needed for subject-wise splits) |
+| `metadata/subject/type` | `[]` | str | -- | Subject type (`human`) |
+| `metadata/subject/age` | `[]` | uint8 | years | Subject age (`0` if not available) |
+| `metadata/subject/sex` | `[]` | str | -- | Subject sex (`M` or `F`) |
+| `metadata/annotations/anatomy` | `[]` | str | -- | Target artery  (`Femoral Vein`) |
+| `metadata/annotations/view` | `[]` | str | -- | View orientation (`Longitudinal`) |
+| `metadata/annotations/label` | `[]` | str | -- | Physiological condition ('Contraction 8Kg HUT 40') |
+| `probe/name` | `[]` | str | -- | Probe identifier (`AL2442`) |
+| `probe/type` | `[]` | str | -- | Probe structure type (`linear`) |
+| `probe/probe_geometry` | `[192, 3]` | float32 | m | Transducer element Cartesian positions (x, y, z) |
+| `data/raw_data` | `[n_frames, n_tx, n_ax, n_el, 1]` | float32 | -- | Raw RF channel data |
+| `data/image` | `[n_frames, z, x]` (+ `coordinates` `[z, x, 3]`) | float32 | dB | Stored log-compressed B-mode intensity (relative to peak) |
+| `data/vector_velocity_x` | `[n_frames, z, x]` (+ `coordinates` `[z, x, 3]`) | float32 | m/s | Lateral component of vector velocity ($v_x$) |
+| `data/vector_velocity_z` | `[n_frames, z, x]` (+ `coordinates` `[z, x, 3]`) | float32 | m/s | Axial component of vector velocity ($v_z$) |
+| `data/vector_velocity_x_deal` | `[n_frames, z, x]` (+ `coordinates` `[z, x, 3]`) | float32 | m/s | Lateral component of dealiased vector velocity ($v_x$) |
+| `data/vector_velocity_z_deal` | `[n_frames, z, x]` (+ `coordinates` `[z, x, 3]`) | float32 | m/s | Axial component of dealiased vector velocity ($v_z$) |
+| `data/power_doppler` | `[n_frames, z, x]` (+ `coordinates` `[z, x, 3]`) | float32 | dB | Power Doppler intensity |
+| `data/color_doppler` | `[n_frames, z, x]` (+ `coordinates` `[z, x, 3]`) | float32 | m/s | Color Doppler map |
+| `scan/*` | -- | -- | -- | Probe geometry, sampling/center/demodulation frequency, t0 delays, sound speed, transmit angles, focus distances, transmit origins, apodizations, PRI... |
+
+All `coordinates` arrays are per-pixel Cartesian positions in meters, last axis
+`[x, y, z]` (y = 0 for 2-D maps).
+
+## Shipped Example Acquisitions
+
+One example acquisition is included under `hdf5/` as a representative subset of
+the full dataset:
+
+| File | Subject | Anatomy | View | Condition | Frames |
+|---|---|---|---|---|---|
+| `hdf5/Acq0.hdf5` | 1 | Femoral Vein | Longitudinal | Contraction 8Kg HUT 40 | 9000-12000 |
+
+Each femoral vein acquisition comprises 2 steered plane-wave transmits (`n_tx = 2`), 2048/3072 axial
+samples, and 192 receive channels. Note that example provided was convrted with start-frame=9000 to get to the interesting part.
+
+The frame count in these examples is truncated for demonstration; full acquisitions contain the frame counts described below.
+
+## Dataset Quantification
+
+Data was collected from 15 participants and consists of 15 acquisitions (one per participant), containing
+24,000 frames of raw RF data per acquisition. Each participant performed isometric plantarflexion 
+contractions at 8 Kg under head up tilt of 40 degrees.
+
+## Subject Metadata
+
+| Metric | Value |
+| :--- | :--- |
+| **Total Number of Subjects** | 15 |
+| **Total Number of Files (Acquisitions)** | 15 |
+| **Sex Composition** | M: 8, F: 7  |
+| **Total RF Frames** | 360,000 |
+
+## Known Issues
+
+- Participants may overlap with other UWaterloo submissions.
+- Due to hardware, some acquisitions may have elevated or spikey Doppler noise on the left side of the image.
+- Some acquisitions may have not hit the target view optimally, leading to poor flow detection.
+- Dealiased velocity vector fields rely on speckle tracking which may be more sensitive to noise in low Doppler power conditions.
+
+## Beamforming and Processing
+
+1. **Pre-Filtering:**
+   Channel RF data is pre-filtered to remove hardware artifacts and out-of-band noise using a 5 MHz bandpass filter before beamforming.
+2. **GPU-Accelerated Beamforming (DAS):**
+   Beamforming is carried out via a GPU-accelerated Delay-and-Sum (DAS) module.
+   - **Aperture & Apodization:** 128-element Hanning window apodization and an F-number of 1.5.
+   - **Dual Angle-Compounding:** Beamforming for B-mode and power Doppler is performed twice with opposite receive angles ($+15^{\circ}$ and $-15^{\circ}$). The final high-resolution beamformed image (HRI) is the average of these two acquisitions:
+     $$HRI = \frac{HRI_{+15^{\circ}} + HRI_{-15^{\circ}}}{2}$$
+   - **Reconstruction Grid:** Cartesian coordinates mapped by a `PixelMap` representing a lateral range of $[-19, 19]\text{ mm}$ and axial depth of $[10, 60]\text{ mm}$ at $0.1\text{ mm}$ spatial resolution.
+3. **Clutter Filtering:**
+   Clutter filtering is performed on the beamformed ensemble using a high-pass wall filter (normalized cut-off frequencies of 0.05 and 0.1, attenuation of 100 dB).
+4. **Multi-Angle Doppler Frequency Estimation:**
+   Angle-specific Doppler frequencies are computed using an ensemble size of 64 frames with a step size of 1.
+   - For conventional vector velocity estimation, we used the following Tx-Rx angles:
+     Tx: [-10, -10, 10, 10]; Rx: [-10, 10, -10, 10]
+   - For dealiased vector velocity estimation, we used the following Tx-Rx angles:
+     Tx: [-10, -10, -10,-10, 10, 10]; Rx: [-10, -3, 6, 10, -6, 3,10]
+   - Color Doppler map is selected as the first of these (Tx: -10, Rx = -10)
+5. **Vector Doppler Velocity Estimation:**
+   Lateral ($v_x$) and axial ($v_z$) velocity components are computed from the multi-angle Doppler frequency estimates using GPU-accelerated least-squares estimation.
+   Lateral ($v_x$) and axial ($v_z$) dealiased velocity components are computed from the multi-angle Doppler frequency estimates using GPU-accelerated extended least-squares estimation.
+
+The full LITMUS processing pipeline (GPU DAS beamforming + multi-angle vector
+Doppler) is documented in [`convert.py`](convert.py). That script is included for
+provenance and reproducibility; it depends on the LITMUS core Python package and
+the raw acquisition frames, so it is not runnable from this folder alone.
+
+Papers relevant to our pipeline:
+
+J. N. Cohen, E. Zafiris, J. N. Jasiak, S. H. T. Ling, O. M. Decyk, H. Nahas, A. C. H. Yu, and J. S. Au, "The influence of aging on graded peripheral venous return and complex blood flow features through human veins." GeroScience (2026). https://doi.org/10.1007/s11357-026-02378-6
+
+H. Nahas, B. Y. S. Yiu, A. J. Y. Chee, T. Ishii and A. C. H. Yu, "Bedside Ultrasound Vector Doppler Imaging System With GPU Processing and Deep Learning," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 72, no. 8, pp. 1079-1094, Aug. 2025, doi: 10.1109/TUFFC.2025.3582773
+
+B. Y. S. Yiu and A. C. H. Yu, "Least-Squares Multi-Angle Doppler Estimators for Plane-Wave Vector Flow Imaging," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 63, no. 11, pp. 1733-1744, Nov. 2016, doi: 10.1109/TUFFC.2016.2582514
+
+## 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 also saves the pipeline to
+[`pipeline.yaml`](pipeline.yaml) as a shareable recipe. Comparing the
+reconstruction against the stored (LITMUS) B-mode is a sanity check that the
+acquisition parameters and probe geometry are recorded correctly, and serves as
+a reproducible reference reconstruction.
+
+When the vector-flow fields (`vector_velocity_x/z`/`vector_velocity_x/z_deal` + `power_doppler`) are present,
+a third panel overlays the vector velocity field on the stored B-mode. The
+overlay uses `draw_velocity_field`, a single self-contained (numpy + matplotlib)
+helper reproduced inside `reconstruct.py` from the LITMUS core Python package
+(`litmus.core_py.visualization`), so the script has no dependency on the full
+LITMUS GPU stack.
+
+The result is written to `reconstruct_output.png`:
+
+![reference reconstruction](reconstruct_output.png)
+
+### Example Usage of reconstruct.py
+
+```bash
+# Reconstruct the default file (hdf5/Acq0.hdf5) at frame 100
+python reconstruct.py
+
+# Reconstruct a specific file and frame, and adjust the power-Doppler mask
+python reconstruct.py --input "hdf5/Acq0.hdf5" --frame 700 --power-threshold 58 --vmax 1
+```
+
+## Ethical Considerations
+
+All human studies were approved by the University of Waterloo’s Human Research
+Ethics Board (ORE #46018). All included data was acquired from participants who
+provided both written and verbal consent prior to participating in the study
+regarding public data sharing.
+
+## Citation
+
+```bibtex
+@article{Cohen2026,
+  author  = {Cohen, Jeremy N. and Zafiris, Eudoxia and Jasiak, Jessica N. and Ling, Skye H. T. and Decyk, Olena M. and Nahas, Hassan and Yu, Alfred C. H. and Au, Jason S.},
+  title   = {The influence of aging on graded peripheral venous return and complex blood flow features through human veins},
+  journal = {GeroScience},
+  year    = {2026},
+  issn    = {2509-2723},
+  doi     = {10.1007/s11357-026-02378-6},
+  url     = {https://doi.org/10.1007/s11357-026-02378-6}
+}
+```

waterloo-largeartery/pipeline.yaml

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+pipeline:
+    operations:
+    -   name: keras.ops.cast
+        params:
+            dtype: float32
+    -   name: band_pass_filter
+        params:
+            passband:
+            - 3000000.0
+            - 7000000.0
+    - demodulate
+    -   name: beamform
+        params:
+            num_patches: 1000
+    - envelope_detect
+    -   name: normalize
+        params:
+            output_range:
+            - 0.0
+            - 1.0
+    - log_compress