Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 49, in _split_generators
                  import h5py
              ModuleNotFoundError: No module named 'h5py'
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 66, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

OpenH-RF Sub-Dataset — Rotational 3D US Raw Channel Data for Elevational SAF (Simulated + Measured Phantom)

One .hdf5 per acquisition, zea file format. Raw per-element channel data (pre-beamforming). See data/manifest.json for the full list and reconstruct.py

  • pipeline.yaml for the reference reconstruction.

Paths in this card are relative to the package root (this file ships as the package README.md; the .hdf5 live in data/). Files marked (source) are part of the authors' generation codebase (MATLAB / Field II / acquisition-system scripts) and are not bundled in this portable package — they are cited for provenance only and are available from the authors on request.

Dataset Description

Synthetic rotational 3D ultrasound acquisitions of point, pair, and off-axis targets, captured with an elevation-focused 1D linear array that is rotated 180° about its axial axis (1° steps, 180 frames). Each acquisition stores the raw per-element channel RF for a single normal plane-wave transmit at every rotation angle — i.e. the data before in-plane beamforming — which is what enables flexible offline beamforming and the elevational Synthetic Aperture Focusing (eSAF) method. The targets span a wide depth range to capture the depth-dependent elevational beam thickness (the artifact eSAF corrects). The release is mostly simulated (Field II), complemented by a small set of real measured phantom rotational scans acquired with the physical Japan Probe 68-element array (same geometry as the simulation) over a shallow-to-focal depth series (10–45 mm). Simulated + measured phantom data (no in-vivo / subjects).

Dataset Contributor(s)

Medical FUSION Laboratory, Worcester Polytechnic Institute. Contact: Ryo Murakami.

Dataset Creation Date

06/15/2026.

License / Terms of Use

CC BY 4.0 (full text in LICENSE). The release contains simulated (Field II) and real measured phantom rotational scans — inanimate phantom only, so there are no IP, subject-consent, or IRB constraints.

Citation. When using this dataset, please cite:

R. Murakami et al., "Elevational Synthetic Aperture Focusing for Rotated Array-Based Three-Dimensional Ultrasound Imaging," IEEE Access, 2025.

Intended Usage

Advanced beamforming and elevational resolution recovery for rotational 3D US (eSAF), elevation-PSF / aperture-growth studies, and as a reproducible raw-channel-data benchmark for rotational synthetic-aperture reconstruction.

Dataset Characterization

  • Data Collection Method: synthetic, generated with Field II (Jensen) run in MATLAB. The main release is a probe × target grid produced by sim/batch_sim_probe_target.m (source) (with sim/sim_probe_catalog.m / sim/sim_target_catalog.m, source): for each (probe type, target) it uses xdc_focused_array
    • calc_scat_multi to produce the raw per-element channel RF at every rotation angle (scatterer rotated about the axial axis, transducer fixed), then sim/sim_dataset_to_zea.py (source) repackages every case into the zea format here. 10 probe types span lateral aperture (n_el 32/68/128, pitch 0.1/0.2/0.3 mm), elevation height H (4/8/12 mm), and elevation focal depth R (25/45/90 mm + unfocused) — see the probe table in data/manifest.json. (The earlier 18-acquisition set generated by sim/batch_generate_fieldii.m + sim/mat_to_zea.py (source) remains available as a compatible alternative with the identical schema.) Measured phantom acquisitions (5): real rotational scans of the physical Japan Probe 68-element array on a wire/point phantom, acquired with CPWC channel-RF capture (experiment/Ryo_SetUp_JP68_PWCompound_3D_ChannelRF.m, source) and a Galil-controlled 180° rotation. Each scan is time-tag-synced (frames → motor angles) and reduced to the single centre (normal) plane wave per angle by experiment/sync_channel_rf.m (source) (so the schema matches the simulation, n_tx = 1), then converted with the same sim/sim_dataset_to_zea.py (source). They span a shallow-to-focal depth series (10, 20, 30, 40, 45 mm nominal target depth).
  • Labeling Method: synthetic ground truth (exact target positions known; in data/manifest.json).
  • Acquisition system (simulated): Japan Probe JP_Linear_68 — 68-element linear array, pitch 0.2 mm, element width 0.15 mm, element height 8 mm, elevational lens focus 45 mm (Field II xdc_focused_array with 500 elevation math sub-elements); center frequency 10 MHz; sampling 40 MHz (NS200BW, 4 samples/wavelength); speed of sound 1490 m/s; single normal plane-wave transmit per rotation angle; 180° rotation, 1° step (180 frames). Parameters match the paper simulation.

Dataset Format

zea file format (HDF5), one file per acquisition, single track: the raw channel RF + scan parameters live in the standard data/scan groups (tracks/track_0 on disk), and the paired eSAF label volume is stored as a zea custom field in the custom group (custom/saf_bmode, read via zea.File.custom — see below). The fused SAF volume is a single frame, so it cannot share the data group with the ~180-frame raw_data (zea validates n_frames across all fields of a data group); the custom group is the zea mechanism for exactly such data, and keeping the file single-track avoids the track_schedule warning a multi-track file would print on every load. Pre-processing — simulated: none beyond the forward model (raw RF, not demodulated/decimated); measured: time-tag frame→angle synchronisation, per-angle dwell averaging, and centre-plane-wave selection (still raw per-element RF, not demodulated/decimated; scan/demodulation_frequency records the 10 MHz demodulation applied by the reference pipeline). The probe rotation per frame is stored as the zea metadata/probe_pose trajectory (rotation_representation="euler_xyz", radians; the array rotates about its axial axis, so the angle is the z Euler component and the translation is zero). Note probe_pose/sampling_frequency = 1.0 Hz is a nominal one-pose-per-frame value, not a physical acquisition rate. Every file is written with zea.File.create() (sim/sim_dataset_to_zea.py + sim/pack_saf_labels.py, source) and carries a zea_version stamp, so zea loads it natively (not as a legacy file).

Paired pre-/post-SAF labels (the dataset's target output). Each file also carries the elevational-SAF reconstructed 3D B-mode volume as the custom field custom/saf_bmode: values is (1, z, x, y) float32 in dB (log-compressed normalized envelope, 0 dB = volume max, empty pixels −inf) and coordinates holds the per-pixel [x, y, z] positions in meters, shape (z, x, y, 3); both carry description/unit attributes. This is the post-SAF output/label paired with the pre-beamformed input (data/raw_data): the raw channel RF is back-projected through the published eSAF algorithm (matlab/saf/safrot_backproj.m, source: in-plane DAS → recon_3dsafrot_backproj, elevational focus 45 mm, f-number 45/8) into a 3D volume B_SAF(x,y,z), generated by sim/make_saf_all.mexperiment/run_esaf_synced.m (source) and written into the zea file by sim/pack_saf_labels.py (source). The stored volume covers a thin depth window (±2 mm) about the target; per-case arc-FWHM before/after and gain are in data/manifest.json and in the description attribute of custom/saf_bmode/values. Read it with zea.File:

with zea.File("data/baseline_R45_H8__point_z080_r4.hdf5") as f:
    saf = {e.name: e for e in f.custom}       # custom/saf_bmode elements
    volume_db   = saf["values"].data          # (1, z, x, y) float32 dB
    coordinates = saf["coordinates"].data     # (z, x, y, 3) float32 m
    print(saf["values"].description)          # axes + eSAF parameters + arc-FWHM

A MATLAB .mat version of the same raw channel data + metadata, plus a reference eSAF-beamformed result and a _ref.png figure, is provided per acquisition alongside the source grid as sim_dataset_out/<probe>/<target>.mat and ..._ref.png (each .mat holds the raw RF, the in-plane DAS, the metadata and the eSAF output produced with the published algorithm matlab/saf/safrot_backproj.m (source): in-plane DAS → recon_3dsafrot_backproj, f-number 45/8). A FWHM-vs-depth overview across probes is sim_dataset_out/dataset_overview_r4.png (sim/dataset_overview.m, source). The zea .hdf5 acquisitions are hosted on Hugging Face at https://huggingface.co/datasets/RyoMurakami/OpenH-RF-eSAF (git-LFS). The MATLAB .mat/_ref.png intermediates are reproducible from source and kept on lab storage.

Dataset Quantification

  • Acquisitions: 195 = 190 simulated + 5 measured phantom.
    • Simulated (190): 10 probe types × 19 targets (16 single points over depth {20,45,80,130} mm × radial offset from the rotation centre {0,2,4,6} mm, plus 3 pair/oblique cases). The probe and target axes are listed in data/manifest.json. (The earlier compatible set has 18 acquisitions.)
    • Measured (5): real rotational phantom scans at nominal depths {10,20,30,40,45} mm (experiment__acq_exp_*.hdf5), centre plane wave, ~182 measured rotation angles over ~180°.
  • Frames per acquisition: simulated 180 (one per 1° step); measured ~182 (the actual encoder angles are stored in metadata/probe_pose — z Euler component, radians — not necessarily uniform).
  • Total size on disk: simulated 0.6–5 MB per case (zea gzip; point-target RF is sparse), measured ~80–92 MB per case (dense tissue RF); **1.5 GB** for the full set (including the paired saf_bmode label volumes).
  • Train/val/test split: N/A (benchmark / characterization set; the probe × depth × radius axes are the intended study dimensions).

Per-sample feature table

Shapes use placeholders because dimensions vary across the probe grid and between simulated and measured scans: n_frames = 180 (simulated, one per 1° step) or ~182 (measured encoder angles); n_el ∈ {32, 68, 128} (probe grid; 68 for the baseline and all measured scans); n_ax = axial sample count (per case); n_z = depth samples of the label volume (target ± ~2 mm window).

Paths below are inside each .hdf5; with zea.File use f.data / f.scan / f.metadata.probe_pose, and f.custom for the SAF label volume.

field (HDF5 path) shape dtype units description
tracks/track_0/data/raw_data (f.data.raw_data) (n_frames, 1, n_ax, n_el, 1) float32 a.u. raw per-element channel RF; dims = (frame=rotation, tx, axial, element, ch)
probe/probe_geometry (n_el, 3) float32 m element positions (lateral x, 0, 0)
tracks/track_0/scan/sampling_frequency scalar float32 Hz 4.0e7
tracks/track_0/scan/center_frequency scalar float32 Hz 1.0e7
tracks/track_0/scan/demodulation_frequency scalar float32 Hz 1.0e7 (= center frequency; used by the reference pipeline's demodulate op)
tracks/track_0/scan/sound_speed scalar float32 m/s 1490
tracks/track_0/scan/initial_times (1,) float32 s t0 (first-sample time)
tracks/track_0/scan/t0_delays (1, n_el) float32 s transmit delays (0; normal plane wave)
tracks/track_0/scan/polar_angles (1,) float32 rad transmit steering (0)
metadata/probe_pose/rotation (n_frames, 3) float32 rad probe pose per frame, euler_xyz; rotation about the axial (z) axis is the z component
metadata/probe_pose/translation (n_frames, 3) float32 m probe tip translation (all zero — pure rotation)
metadata/probe_pose/sampling_frequency scalar float32 Hz 1.0 — nominal one-pose-per-frame rate, not a physical value
metadata/credit (f.metadata.credit) scalar str dataset credit / attribution (lab, contact, citation, license)
metadata/subject/type (f.metadata.subject.type) scalar str simulated phantom (Field II sims) or phantom (measured experiment__* scans)
custom/saf_bmode/values (custom field, via f.custom) (1, n_z, n_el, n_el) float32 dB paired label: elevational-SAF reconstructed 3D B-mode volume, log-compressed normalized envelope (0 dB = max, empty pixels −inf); dims = (frame, z=depth, x=lateral, y=elevation)
custom/saf_bmode/coordinates (n_z, n_el, n_el, 3) float32 m per-pixel [x, y, z] positions of the label volume (target ± ~2 mm depth window)

Subject Metadata

No human or animal subjects / no PHI. Each file stores metadata/subject/type: simulated phantom for the Field II simulations, phantom for the measured experiment__* scans. Creator attribution is stored per file in metadata/credit.

Data Validation

reconstruct.py (runnable, verified — official zea API, no fallback code) loads one zea acquisition, reads its acquisition parameters via zea.Config.from_path('pipeline.yaml') + File.load_parameters, beamforms the rotation frame closest to ±90° rotation magnitude (the frame where an off-axis target lies in-plane; this handles signed encoder angles too — measured scans run 0 → ~−180°) with the native zea.Pipeline op chain Cast → Demodulate → Beamform(delay_and_sum) → EnvelopeDetect → Normalize → LogCompress defined in pipeline.yaml, and writes a two-panel PNG: the B-mode image, and the per-frame probe rotation angle (from metadata/probe_pose, plotted in degrees) so downstream users know how to interpret the frame axis — the special data this dataset adds:

python reconstruct.py --input data/baseline_R45_H8__point_z080_r4.hdf5 --output out.png

The rotational eSAF across frames — the contribution of this dataset — is implemented in matlab/saf (source) (recon_3dsafrot_backproj); per-probe before/after eSAF reference images and a FWHM-vs-depth overview accompany the MATLAB .mat release (sim/dataset_overview.m, source), and the resulting paired SAF volume is stored as the custom/saf_bmode custom field of every .hdf5 (see Dataset Format above).

Known Issues

  • Paired SAF label — on-axis targets (r0 = 0) do not narrow, by design. eSAF refocuses the rotational elevation smear; a target sitting on the rotation axis has essentially no smear, so its saf_bmode label volume is not sharper than the input (arc-FWHM gain ≈ 1). This is expected physics, not a defect — the 40 on-axis cases (median gain 1.00×) are included so the pair covers the degenerate no-smear case. Off-axis targets (n=120, median gain 1.75×, up to 12×) and paired/oblique targets (n=30, median 3.71×) improve clearly; targets at the focal depth (45 mm) and weak-elevation-focus probes (efocus_deep_90, elev_unfocused) have less smear to recover. Across all 195 cases, median arc-FWHM gain is 1.36× (42 cases < 1×, mostly the on-axis/near-focus group above). Arc-FWHM is measured on a centred reconstruction: the smear circle passes through both the rotation axis and the target (not a circle centred on the rotation axis). The eSAF back-projection uses a fixed elevational focus of 45 mm; per-depth focus tuning (see docs/eSAF_focus_depth_study_JP.md, source) can further sharpen deep off-axis cases but was not applied here (single as-designed focus).
  • Measured phantom depth window. The real reflector bead sits ~4 mm off the rotation axis (not on-axis) and, for each scan, slightly deeper than the folder's nominal depth label; labels are reconstructed over the interactively-identified reflector depth window (not a naive nominal-depth ± 2 mm window), which matters because a mis-centred window can pick up near-axis clutter instead of the actual bead.
  • Simulated data (Field II spatial-impulse-response model): realistic transducer field, but no tissue attenuation, aberration, multiple scattering, or electronic noise. Not a substitute for measured data.
  • Speed of sound is 1490 m/s, matching the paper Table 1 and the experiment.
  • A single normal plane-wave transmit per rotation angle is simulated (the dataset stores n_tx = 1); multi-angle compounding is left to downstream users.
  • Measured scans: acquired as 7-angle CPWC; only the centre (0°) plane wave is kept here to match the n_tx = 1 schema. The dwell frames per angle are averaged before storage (noise reduction). Real reflectors are not ideal point scatterers — expect reverberation/clutter near the surface and specular layering; rotation angles are the measured encoder values (slightly non-uniform, full span ≈ 180°, sign per encoder direction). The elevational lens focus is the nominal 45 mm, but the effective back-projection focus for eSAF is depth-dependent on real data (see docs/eSAF_focus_depth_study_JP.md, source).

Raw Source Data

The raw, pre-conversion acquisition/simulation outputs that were processed into the zea .hdf5 files above are archived (same CC BY 4.0 license) at https://huggingface.co/datasets/RyoMurakami/OpenH-RF-eSAF-raw: the raw Verasonics per-line channel-RF captures (RFdata_line*.mat + encoder logs) for the 5 measured acquisitions, and the per-case MATLAB intermediates (raw RF, in-plane DAS, eSAF output) for the 190 simulated cases. See that repository's README for how each maps to data/*.hdf5 here.

Ethical Considerations

None. The data is either fully synthetic (Field II) or measured on an inanimate phantom — no human or animal subjects, no PHI, no consent/IRB constraints.

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