stanford-murine: add hero and main images, sync card and pipelines with GitHub

stanford-murine/README.md

@@ -23,6 +23,10 @@ size_categories:
 
 # Stanford Murine Liver and Sound-Speed Phantom Ultrasound
 
+![B-mode reconstructions of a rat liver and the ATS 549 phantom](assets/hero.png)
+
+*Frame 0 reconstructed with `reconstruct.py` (L12-3v, 60 dB). Top: the exposed liver of [Rat3](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/stanford-murine/data/RatExperiments/VerasonicsAcq/Rat3/ExposedLiver/DATA_Tracks_20190319_115804.hdf5), `multifocal` track. Bottom: the ATS 549 phantom's [lesions](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/stanford-murine/data/SoSExperiments/august13_2020/L12_3v/ATS_phantom/lesion/DATA_Tracks_20200813_164505.hdf5) and [point targets](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/stanford-murine/data/SoSExperiments/august13_2020/L12_3v/ATS_phantom/point_target/DATA_Tracks_20200813_164745.hdf5), `hadamard` track. The point targets curve into arcs because the phantom is beamformed at 1540 m/s while its own sound speed is lower — the mismatch this dataset is built to estimate and correct.*
+
 ## Dataset Description
 
 This dataset contains pre-beamformed pulse-echo ultrasound channel data from murine livers and sound-speed phantoms. The data were acquired on a Verasonics Vantage 256 using multifocal, Hadamard-encoded, and full synthetic aperture (FSA) transmit sequences. The dataset supports research on beamforming, sound-speed estimation, and aberration correction; it is not intended for clinical diagnosis.
@@ -99,7 +103,19 @@ Each output file bundles three tracks named `multifocal`, `hadamard`, and `synth
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/stanford-murine/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline_multifocal.yaml`, `pipeline_hadamard.yaml` and `pipeline_synthetic_aperture.yaml` definitions in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; each file bundles the three tracks, and the script reconstructs every track with its own pipeline and writes one image per track to `assets/`.
+The acquisitions can be processed with the `pipeline_multifocal.yaml`, `pipeline_hadamard.yaml` and `pipeline_synthetic_aperture.yaml` definitions in this folder and the [zea library](https://github.com/tue-bmd/zea). Each file bundles three tracks, one per transmit sequence (`multifocal`, `hadamard`, `synthetic_aperture`), and each track has its own pipeline.
+
+`zea` streams the data from the Hugging Face Hub and processes it according to the pipeline; pass the matching track with `--track`. You can try it out with the following command:
+
+```bash
+zea process \
+  --dataset hf://nvidia/OpenH-RF/stanford-murine/data/RatExperiments/VerasonicsAcq/Rat3/ExposedLiver/DATA_Tracks_20190319_115804.hdf5 \
+  --config hf://nvidia/OpenH-RF/stanford-murine/pipeline_synthetic_aperture.yaml \
+  --track synthetic_aperture \
+  --n-frames 1
+```
+
+Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/stanford-murine/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF). It reconstructs the first frame of every track in `ZEA_FILE` with the same pipelines and writes one image per track to `assets/`. On top of the YAML, the script inserts a baseband FIR low-pass after demodulation, with its cutoff at half the probe bandwidth (4.0 MHz for L12-3v, 3.0 MHz for L12-5, 1.3 MHz for C5-2v). The cutoff depends on the probe, so it is not part of the pipeline YAMLs and `zea process` runs without it; the difference is small on the rat acquisitions and clearly visible at depth on the phantoms.
 
 ## Dataset Format
 
@@ -176,30 +192,7 @@ The phantom data contain no human subjects. Phantom identifiers describe the pro
 
 ## Data Validation
 
-The reconstruction path uses the first stored frame and a zea `Pipeline`: RF demodulation and baseband FIR filtering, delay-and-sum beamforming, envelope detection, maximum normalization, and log compression. The pipeline YAML files use three pixels per acoustic wavelength and disable pressure-field weighting. Beamforming is split into bounded patches so the deepest C5-2v grid fits on a 24 GB GPU.
-
-From the repository root, generate the demo files and reference images with:
-
-```bash
-python examples/stanford/download.py --demo
-python examples/stanford/convert.py --dataset rat --demo
-python examples/stanford/convert.py --dataset phantom --demo
-
-CUDA_VISIBLE_DEVICES=0 JAX_PLATFORMS=cuda KERAS_BACKEND=jax \
-  python examples/stanford/reconstruct.py --dataset rat --demo --rat-id 9
-CUDA_VISIBLE_DEVICES=0 JAX_PLATFORMS=cuda KERAS_BACKEND=jax \
-  python examples/stanford/reconstruct.py --dataset phantom --demo
-
-python examples/stanford/stitch.py --dataset rat --demo --rat-id 9
-python examples/stanford/stitch.py --dataset phantom --demo
-```
-
-The reconstructed PNGs and demodulated spectra are written under `examples/stanford/outputs`. The stitched review documents are:
-
-- `examples/stanford/outputs/RatExperiments/all_bmode_reconstructions.pdf`
-- `examples/stanford/outputs/SoSExperiments/all_sos_bmode_reconstructions.pdf`
-
-The reconstruction scripts are validated with zea 0.1.4.
+The reference pipelines reconstruct the first stored frame of each track: RF demodulation, delay-and-sum beamforming without pressure-field weighting, envelope detection, maximum normalization and log compression, shown over a 60 dB dynamic range. The grid is derived at three pixels per acoustic wavelength over the acquired aperture and depth. `reconstruct.py` adds the probe-dependent baseband FIR described in [Processing the Dataset](#processing-the-dataset); without it, `zea process` and `reconstruct.py` produce the same image. The figure at the top of this card shows the `multifocal` track of Rat3 and the `hadamard` track of two ATS 549 acquisitions, reconstructed with `reconstruct.py`. `assets/main.png` is the same Rat3 `multifocal` reconstruction on its native grid, without axes.
 
 ## Known Issues
 
@@ -215,48 +208,6 @@ No human data are included. The murine study was approved by Stanford University
 
 The phantom acquisitions require no human- or animal-subject approval. Both Figshare records confirm that no human personally identifiable information is present.
 
-## Scripts and Paths
-
-- `download.py`: download the raw Figshare demo subset or full records.
-- `download_figshare_full.sh`: resumably mirror both full Figshare records to `/ultra20/figshare` with progress and checksum verification.
-- `convert.py`: convert source MAT files to zea HDF5.
-- `reconstruct.py`: reconstruct HDF5 tracks to B-mode PNGs and spectra.
-- `stitch.py`: combine PNGs into review PDFs.
-- `upload.py`: upload selected HDF5 files, this README, and pipeline YAMLs.
-- `delete_hf.py`: delete existing remote `zea/` files before replacement.
-
-Default paths:
-
-- Raw rat input: `examples/stanford/data/RatExperiments`
-- Raw phantom input: `examples/stanford/data/SoSExperiments`
-- Converted rat output: `examples/stanford/data/RatExperiments_zea`
-- Converted phantom output: `examples/stanford/data/SoSExperiments_zea`
-- Reconstruction output: `examples/stanford/outputs`
-
-For full conversion after downloading both Figshare records:
-
-```bash
-python examples/stanford/download.py --full
-python examples/stanford/convert.py --dataset rat --full
-python examples/stanford/convert.py --dataset phantom --full
-python examples/stanford/reconstruct.py --dataset rat --full
-python examples/stanford/reconstruct.py --dataset phantom --full
-python examples/stanford/stitch.py --dataset rat --full
-python examples/stanford/stitch.py --dataset phantom --full
-```
-
-To resume an interrupted conversion without rewriting completed bundles, add `--skip-existing`. A bundle is skipped only when both its atomic HDF5 output and text summary are present.
-
-To keep a separate, resumable mirror of both complete Figshare records under `/ultra20/figshare`, with per-file and overall progress bars plus size and MD5 verification, run:
-
-```bash
-examples/stanford/download_figshare_full.sh
-```
-
-This writes `RatExperiments_download` and `SoSExperiments_download`. Use `examples/stanford/download_figshare_full.sh --dry-run` to validate and summarize the public records without downloading them.
-
-Use `--min-rat N` or `--rat-id N` with `convert.py` to limit rat conversion. Raw MAT files remain local; `upload.py` selects converted HDF5 files and documentation explicitly.
-
 ## Citation
 
 Please cite the applicable source dataset and its associated publication:

stanford-murine/pipeline_hadamard.yaml

@@ -3,14 +3,9 @@ pipeline:
     operations:
     # Overlap averaging creates fractional RF, and Hadamard decoding can exceed int16.
     - demodulate
-    -   name: fir_filter
-        params:
-            axis: -3
-            complex_channels: true
     -   name: beamform
         params:
             enable_pfield: false
-            # Bound work units so the deep C5-2v grid fits on a 24 GB GPU.
     - envelope_detect
     # Use the true maximum; percentile normalization would clip strong reflectors.
     -   name: normalize
@@ -23,3 +18,6 @@ parameters:
     sound_speed: 1540
     # Derive both grid dimensions at three samples per acoustic wavelength.
     pixels_per_wavelength: 3
+    dynamic_range:
+    - -60
+    - 0

stanford-murine/pipeline_multifocal.yaml

@@ -3,14 +3,9 @@ pipeline:
     operations:
     # Coherent overlap averaging can create fractional RF samples; no cast is needed.
     - demodulate
-    -   name: fir_filter
-        params:
-            axis: -3
-            complex_channels: true
     -   name: beamform
         params:
             enable_pfield: false
-            # Bound work units so the deep C5-2v grid fits on a 24 GB GPU.
     - envelope_detect
     # Use the true maximum; percentile normalization would clip strong reflectors.
     -   name: normalize
@@ -23,3 +18,6 @@ parameters:
     sound_speed: 1540
     # Derive both grid dimensions at three samples per acoustic wavelength.
     pixels_per_wavelength: 3
+    dynamic_range:
+    - -60
+    - 0

stanford-murine/pipeline_synthetic_aperture.yaml

@@ -4,14 +4,9 @@ pipeline:
     # Coherent overlap averaging can create fractional RF samples; no cast is needed.
     - apply_window
     - demodulate
-    -   name: fir_filter
-        params:
-            axis: -3
-            complex_channels: true
     -   name: beamform
         params:
             enable_pfield: false
-            # Bound work units so the deep C5-2v grid fits on a 24 GB GPU.
     - envelope_detect
     # Use the true maximum; percentile normalization would clip strong reflectors.
     -   name: normalize
@@ -24,3 +19,6 @@ parameters:
     sound_speed: 1540
     # Derive both grid dimensions at three samples per acoustic wavelength.
     pixels_per_wavelength: 3
+    dynamic_range:
+    - -60
+    - 0