oslo/README.md

metadata

pretty_name: OpenH-RF — UltraSound ToolBox (USTB) Channel Capture Collection
license: cc-by-4.0
task_categories:
  - image-to-image
language:
  - en
tags:
  - ultrasound
  - rf
  - openh-rf
  - beamforming
  - channel-data
size_categories:
  - n<1K

UltraSound ToolBox (USTB) Channel Capture Collection — OpenH-RF Submission

Contributed by the University of Oslo (UiO), Department of Informatics (the USTB team) to the OpenH-RF initiative. All data is pre-beamformed (raw channel-capture) ultrasound stored in the zea HDF5 format (zea_version 0.1.6, meeting the OpenH-RF minimum format version 0.1.4) and released under CC BY 4.0.

OpenH-RF Release Inventory

Current OpenH-RF release: 39 HDF5 files; 8.61 GB (8,613,593,088 bytes) stored; root zea_version 0.1.6. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.

Contents

39 acquisitions packaged into six application sub-datasets. Each sub-dataset folder contains its zea .hdf5 files, a Hugging Face–style README.md data card, and one reference B-mode PNG per acquisition. The reconstruct.py, pipeline.yaml, and the CC BY 4.0 LICENCE live once at the submission root — the reconstruction reconstructs every sub-dataset (the pipeline is identical across folders), and the single LICENCE covers the whole collection (each data card also declares license: cc-by-4.0 in its YAML frontmatter).

Folder	Sub-dataset	Tier	RFP task	Acq.
A_cardiac/	In-vivo cardiac (Verasonics P4-2)	in-vivo human (research)	6.1 Generalized Reconstruction	2
B_carotid/	In-vivo carotid (Verasonics L7-4)	in-vivo human (research)	6.1 Generalized Reconstruction	3
C_verasonics_phantom/	Phantom (Verasonics L7-4 / P4)	phantom	6.1 Generalized Reconstruction	15
D_alpinion_phantom/	Phantom (Alpinion L3-8)	phantom	6.1 Generalized Reconstruction	4
E_simulation/	Simulation (Field II)	simulation	6.1 Generalized Reconstruction	11
F_motion/	Motion estimation (SWE / ARFI, L7-4)	phantom	6.4 Motion Estimation	4

Total: 39 acquisitions, 8.61 GB of stored HDF5 data.

How to reconstruct

Every acquisition is reconstructable from the file alone — all acquisition parameters live in the zea /scan and /probe groups. A single reconstruct.py at the submission root reconstructs every acquisition in every sub-dataset folder:

export KERAS_BACKEND=jax
python reconstruct.py                          # standard recon for every .hdf5
python reconstruct.py --refocus                # also emit REFoCUS where enabled
python reconstruct.py A_cardiac/<file>.hdf5    # a single acquisition

All reconstruction choices live in parameters.yaml — one entry per acquisition giving its pipeline (which zea.Pipeline to use), display window (zlims/xlims in mm) and dynamic_range. Every acquisition uses the same workflow (load parameters → run the pipeline → plot); there is no per-file logic in the script. Each pipeline value maps to a zea.Pipeline YAML at the root:

pipeline	Used for	zea.Pipeline
scanline	focused linear (FI) scans	pipeline_scanline.yaml: beamform on a grid_type: scanline grid with enable_receive_apodization: true — one focused transmit per image line. The artifact-free reconstruction for a walking-focus linear scan (compounding focused beams onto a shared grid leaves a band at the focal depth).
sector	phased-array / steered focused sector scans	pipeline_sector.yaml: polar grid + scan conversion; pressure-field-weighted DAS with peaked weighting (≈ scanline) and focal_region_length.
iq	baseband IQ (n_ch == 2, e.g. PICMUS)	pipeline_iq.yaml (no demodulation step).
compound	non-focused linear (plane-wave / diverging / STA / SWE-ARFI)	pipeline.yaml: coherent compounding, no pfield (these insonify the whole field of view).

The pipelines share the same shape as zea's regular B-mode pipeline (cast → apply_window → demodulate → beamform → envelope → normalize → log_compress). Scanline imaging uses the same beamform op with a scanline grid plus receive apodization rather than a dedicated scanline-specific operation. Two zea features used in this submission are scanline receive apodization (enable_receive_apodization) and focal_region_length (focal-region delay blending, Rindal et al., IUS 2018).

An acquisition may also set refocus: true in parameters.yaml. When reconstruct.py is run with the --refocus flag (off by default), it additionally runs a REFoCUS reconstruction for those acquisitions — transmit-encoding recovery (Bottenus, 2018) that inverts the transmit-encoding matrix to recover the multistatic (full-matrix-capture) dataset before pressure-field-weighted DAS — and writes a <name>_zea_refocus_bmode.png alongside the standard reconstruction. REFoCUS is enabled for every acquisition with enough encoded transmits for the inversion to be well posed (≥ 8 transmit events) — coherent plane-wave compounding (CPWC), diverging waves (DW), and focused linear (FI) and phased-array sector scans. Phased-array sector acquisitions use pipeline_refocus_sector.yaml (polar grid + scan conversion, preserving the sector geometry); every other geometry uses the linear/cartesian pipeline_refocus.yaml. REFoCUS is not enabled where the inversion is ill-posed or undefined: single/few-transmit acquisitions (the n_tx = 1 plane-wave tracking sets and single-angle simulations, PICMUS's 5-angle IQ) and synthetic transmit aperture (STA) acquisitions, which are already multistatic.

Reference images

Each acquisition ships with:

• <name>_zea_bmode.png — produced by the root reconstruct.py (zea); the reproducible reference.
• <name>_zea_bmode_old.png — the initial v2 zea reconstruction (kept for comparison).
• <name>_bmode.png — the canonical USTB MATLAB Delay-And-Sum reconstruction from the public USTB dataset catalog, for cross-validation.
• <name>_zea_refocus_bmode.png — REFoCUS variant, for acquisitions with refocus: true (CPWC / DW / focused FI / sector with ≥ 8 transmits; not STA or single/few-transmit sets).

Licensing & attribution

Released under CC BY 4.0. Please cite the UltraSound ToolBox (USTB), University of Oslo (Zenodo record 20261898). PICMUS_numerical_calib_v2 (group E) was created in collaboration with our group as part of the PICMUS effort and is included on that basis; the other PICMUS datasets are excluded.

Contact

Ole Marius Hoel Rindal — omrindal@ifi.uio.no (UiO, USTB).