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stanford-murine/README.md

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+---
+license: apache-2.0
+pretty_name: Stanford Murine Liver and Sound-Speed Phantom Ultrasound
+task_categories:
+- image-to-image
+- feature-extraction
+tags:
+- ultrasound
+- rf
+- openh-rf
+- beamforming
+- sound-speed-estimation
+- aberration-correction
+- murine-liver
+- phantom
+- verasonics
+language:
+- en
+size_categories:
+- n<1K
+---
+
+# Stanford Murine Liver and Sound-Speed Phantom Ultrasound
+
+## 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.
+
+The source data are available from Figshare:
+
+- [Murine liver acquisitions](https://doi.org/10.25452/figshare.plus.28291985)
+- [Sound-speed phantom and meat-layer acquisitions](https://doi.org/10.25452/figshare.plus.28291988)
+
+The converted demo dataset is hosted at
+[openh-rf/stanford](https://huggingface.co/datasets/openh-rf/stanford).
+
+## Dataset Contributors
+
+The source datasets were created at Stanford University by Arsenii V.
+Telichko, Rehman Ali, Andrew Andrzejek, Thurston Brevett, Benjamin N. Frey,
+Brian Boitnott, Jihye Baek, Louise Zhuang, Hoda Hashemi, Jun Hong Park, Caelia
+Thomas, and Jeremy Dahl. The contributing organization is the Dahl Lab,
+Stanford University. Contact: `jjdahl@stanford.edu`.
+
+## Dataset Creation Date
+
+06/18/2025, the publication date of both source Figshare records.
+
+## License / Terms of Use
+
+The source Figshare records and this converted dataset are distributed under
+the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). Both
+Figshare records state that the files may be publicly distributed under the
+selected license.
+
+The OpenH-RF evaluation template currently names CC BY 4.0 as its expected
+license. This data card preserves the verified upstream Apache 2.0 declaration;
+changing the dataset license would require confirmation from the contributors
+and Stanford rather than a documentation-only edit.
+
+## Intended Usage
+
+The data are intended for development and validation of:
+
+- pulse-echo ultrasound beamforming;
+- local and global sound-speed estimation;
+- aberration correction through tissue or meat layers; and
+- comparisons among multifocal, Hadamard-encoded, and FSA acquisition schemes.
+
+The provided pipeline is a reference delay-and-sum B-mode reconstruction, not
+a prescribed preprocessing pipeline for model training.
+
+## Dataset Characterization
+
+### Data Collection Method
+
+The collection combines in-vivo and post-mortem animal acquisitions with
+table-top phantom acquisitions:
+
+- The murine study contains liver scans from 20 Zucker rats: 4 lean controls
+  and 16 obese animals used as a model of hepatic steatosis.
+- The phantom study contains an ATS 549 phantom and six agarose/graphite
+  phantoms with varying n-propanol concentration. Many acquisitions include a
+  10–15 mm porcine or galline meat layer as a controlled aberrator.
+
+### Labeling Method
+
+No image, pixel-wise segmentation, or manual annotation labels are supplied.
+Rat-level sound-speed, fat-percentage, and histopathology measurements are
+derived from the source quantification workbooks when available. Phantom
+sound speeds were measured independently with a through-transmission setup.
+
+### Acquisition System
+
+All acquisitions use a Verasonics Vantage 256 research scanner. The converted
+demo covers:
+
+| Probe | Geometry | Elements | Center frequency | Sampling frequency |
+|---|---|---:|---:|---:|
+| C5-2v | Curved | 128 | 4.0 MHz | 15.625 MHz |
+| L12-3v | Linear | 192 | 6.0 or 7.813 MHz | 25.0 or 31.25 MHz |
+| L12-5 | Linear | 256 | 7.5 MHz | 31.25 MHz |
+
+Each output file bundles three tracks named `multifocal`, `hadamard`, and
+`synthetic_aperture`. Transmit delays, transmit apodization, focal distances,
+origins, receive initial times, probe geometry, acquisition sound speed, and
+sampling frequencies are read from the corresponding MAT file.
+For the rat acquisitions, the Verasonics sound-speed setting is 1540 m/s; this
+is retained in `scan/sound_speed` for reconstruction. It is distinct from the
+post-acquisition liver sound-speed measurements described below.
+
+## Dataset Format
+
+Converted data use the zea HDF5 representation. Channel samples have dimension
+order:
+
+```text
+(n_frames, n_tx, n_ax, n_el, n_ch)
+```
+
+The converter:
+
+1. reads every acquired Verasonics receive-buffer frame, limited to the sample
+   interval referenced by each sequence's Receive metadata;
+2. coherently averages duplicate physical elements from overlapping receive
+   apertures while retaining full amplitude for elements recorded once;
+3. decodes the positive/negative Hadamard acquisitions;
+4. trims axial samples that are zero across every frame, transmit, element, and
+   channel; and
+5. stores the resulting RF channel data as `float32`.
+
+The `float32` dtype is intentional. Coherent aperture averaging can create
+half-integer samples, and Hadamard decoding can exceed the `int16` range.
+Saving decoded RF as `int16` would round or overflow valid samples.
+
+Reconstructed B-mode images and segmentation masks are not stored in the
+converted files. They are generated separately by the validation pipeline.
+
+## Dataset Quantification
+
+The demo conversion contains four HDF5 acquisition bundles: one Rat9 bundle
+and one phantom bundle for each of C5-2v, L12-3v, and L12-5. Each bundle has
+three sequence tracks, and each track retains every frame in its source MAT
+file. The current demo has 12 tracks and 64 frames and occupies approximately
+5.5 GiB; HDF5 writer and compression versions may change the exact byte count.
+
+The two complete source Figshare records contain approximately 491.9 GB in
+total. No train, validation, or test split is defined. Full conversion size and
+the number of successfully grouped acquisition triplets depend on the locally
+downloaded source tree; the scripts do not assign learning splits.
+
+Representative `raw_data` shapes in the demo are:
+
+| Acquisition | Multifocal | Hadamard | FSA |
+|---|---|---|---|
+| Rat9, L12-3v | `(2, 576, 847, 192, 1)` | `(2, 192, 847, 192, 1)` | `(10, 192, 847, 192, 1)` |
+| Phantom, C5-2v | `(10, 256, 2432, 128, 1)` | `(10, 128, 2432, 128, 1)` | `(10, 128, 2432, 128, 1)` |
+| Phantom, L12-3v | `(2, 576, 1613, 192, 1)` | `(2, 192, 1613, 192, 1)` | `(10, 192, 1613, 192, 1)` |
+| Phantom, L12-5 | `(2, 512, 2018, 256, 1)` | `(2, 256, 2018, 256, 1)` | `(2, 256, 2018, 256, 1)` |
+
+The Rat9 multifocal sequence uses the three focal depths programmed in the
+source MAT file: 17.74, 29.57, and 41.39 mm. Its programmed imaging range
+extends to 50.46 mm, so all three foci are intentional. The stored RF tensor
+ends near 21.24 mm because samples beyond that depth are zero across every
+transmit and receive channel and the converter removes this all-zero tail.
+The focal metadata are retained even when a focus lies beyond the remaining
+nonzero RF support.
+
+Core per-track features are:
+
+| Name | Shape | Dtype | Units | Description |
+|---|---|---|---|---|
+| `data/raw_data` | `(n_frames, n_tx, n_ax, n_el, n_ch)` | `float32` | arbitrary RF units | Decoded pre-beamformed channel data |
+| `scan/initial_times` | `(n_tx,)` | `float32` | s | Receive A/D start time for each transmit |
+| `scan/t0_delays` | `(n_tx, n_el)` | `float32` | s | Per-element transmit delays |
+| `scan/tx_apodizations` | `(n_tx, n_el)` | `float32` | unitless | Per-element transmit weights and Hadamard polarity |
+| `scan/focus_distances` | `(n_tx,)` | `float32` | m | Transmit focal distance |
+| `scan/transmit_origins` | `(n_tx, 3)` | `float32` | m | Transmit origins in `(x, y, z)` order |
+| `scan/sampling_frequency` | scalar | `float32` | Hz | RF sampling frequency |
+| `scan/center_frequency` | scalar | `float32` | Hz | Transmit center frequency |
+| `scan/demodulation_frequency` | scalar | `float32` | Hz | Carrier used for RF-to-IQ demodulation |
+| `scan/sound_speed` | scalar | `float32` | m/s | Verasonics acquisition/reconstruction sound speed |
+| `probe/probe_geometry` | `(n_el, 3)` | `float32` | m | Element coordinates in `(x, y, z)` order |
+
+## Subject Metadata
+
+The source murine cohort contains 4 lean Zucker rats (2 male and 2 female) and
+16 obese Zucker rats (8 male and 8 female), beginning at 13 weeks of age. The
+obese animals were fed a high-fat diet for up to eight weeks. Converted rat
+files store the rat identifier and, when present in the source workbooks, sex,
+fat percentage, measured liver sound speed, weight, diet duration, and
+histopathology-derived values.
+
+Values with standard DataSpec fields are stored under `metadata/subject`:
+
+| Name | Dtype | Units | Description |
+|---|---|---|---|
+| `fat_percentage` | `float32` | % | Mean measured liver fat percentage |
+| `weight` | `float32` | kg | Animal weight, converted from workbook grams |
+| `sex` | string | unitless | Sex reported by the source workbook |
+
+Other workbook measurements are scalar `float32` datasets under
+`custom/rat_quantification/`, each with `unit` and `description` attributes.
+These include the ground-truth sound-speed mean and standard deviation,
+sequence-specific local and global sound-speed estimates, liver-lobe
+sound-speed measurements when available, diet duration, fat-percentage
+uncertainty, water temperature, and quantitative histopathology scores. The
+workbook's non-fat percentage is omitted because it is exactly
+`100 - metadata/subject/fat_percentage` for every rat. These measurements are
+scalars rather than homogeneous `sos_map` images because the source provides
+no spatial coordinates for these rat-level measurements.
+
+The phantom data contain no human subjects. Phantom identifiers describe the
+probe, material configuration, meat layer, and acquisition session. The source
+records report independently measured phantom and meat-layer sound speeds and
+temperature-dependent uncertainties.
+
+## 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.
+
+## Known Issues
+
+- The demo MAT files store `Receive/TGC` profile index 1 but do not store the
+  selected profile's `TGC/Waveform`. A profile index is not a gain curve, so
+  the converter omits the optional `scan/tgc_gain_curve` field rather than
+  inventing a unity curve. The RF samples already reflect acquisition-time
+  TGC, but that gain cannot be calibrated or undone from these files alone.
+- Respiratory motion affected early in-vivo rat acquisitions. Later
+  acquisitions were made shortly after euthanasia, as described by the source
+  record and associated publication.
+- The source record notes settling and slight inhomogeneity in phantom 4 and
+  phantom 5.
+- Strong meat/phantom interfaces and curved-probe reverberation can dominate
+  individual B-mode images. Reconstruction limits preserve the acquired depth
+  rather than cropping those regions automatically.
+- The data contain no image or segmentation ground truth.
+
+## Ethical Considerations
+
+No human data are included. The murine study was approved by Stanford
+University's Institutional Administrative Panel on Laboratory Animal Care.
+Animals were scanned under 2% isoflurane anesthesia on a heated platform with
+continuous temperature monitoring; the associated publication describes the
+euthanasia and tissue-measurement procedures. The public sources do not state
+an approval number or explicit ARRIVE 2.0 compliance.
+
+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:
+
+- Telichko, A. V. et al. (2025). *Pulse-Echo Ultrasound Murine In Vivo
+  Acquisitions*. Figshare+. <https://doi.org/10.25452/figshare.plus.28291985.v1>
+- Ali, R. et al. (2025). *Pulse-Echo Ultrasound Sound Speed Phantom and Meat
+  Aberrating Layer Acquisitions*. Figshare+.
+  <https://doi.org/10.25452/figshare.plus.28291988.v1>
+- Telichko, A. V. et al. “Noninvasive Estimation of Local Speed of Sound by
+  Pulse-Echo Ultrasound in a Rat Model of Nonalcoholic Fatty Liver.” *Physics
+  in Medicine & Biology* 67(1), 015007 (2022).
+  <https://doi.org/10.1088/1361-6560/ac4562>
+- Ali, R. et al. “Local Sound Speed Estimation for Pulse-Echo Ultrasound in
+  Layered Media.” *IEEE TUFFC* 69(2), 500–511 (2022).
+  <https://doi.org/10.1109/TUFFC.2021.3124479>

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