Datasets:
The dataset viewer is not available for this subset.
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
.hdf5per acquisition, zea file format. Raw per-element channel data (pre-beamforming). Seedata/manifest.jsonfor the full list andreconstruct.py
pipeline.yamlfor the reference reconstruction.Paths in this card are relative to the package root (this file ships as the package
README.md; the.hdf5live indata/). 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) (withsim/sim_probe_catalog.m/sim/sim_target_catalog.m, source): for each (probe type, target) it usesxdc_focused_arraycalc_scat_multito produce the raw per-element channel RF at every rotation angle (scatterer rotated about the axial axis, transducer fixed), thensim/sim_dataset_to_zea.py(source) repackages every case into the zea format here. 10 probe types span lateral aperture (n_el32/68/128, pitch 0.1/0.2/0.3 mm), elevation heightH(4/8/12 mm), and elevation focal depthR(25/45/90 mm + unfocused) — see the probe table indata/manifest.json. (The earlier 18-acquisition set generated bysim/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 byexperiment/sync_channel_rf.m(source) (so the schema matches the simulation, n_tx = 1), then converted with the samesim/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_arraywith 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_3d → safrot_backproj,
elevational focus 45 mm, f-number 45/8) into a 3D volume B_SAF(x,y,z), generated by
sim/make_saf_all.m → experiment/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_3d → safrot_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°.
- 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
- 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 pairedsaf_bmodelabel 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_3d → safrot_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_bmodelabel 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 to12×) 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 (seedocs/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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