oslo/README.md

metadata

name: oslo
pretty_name: UltraSound ToolBox (USTB) Channel Capture Collection
license: cc-by-4.0
task_categories:
  - image-to-image
language:
  - en
tags:
  - ultrasound
  - rf
  - open-h-rf
  - beamforming
  - channel-data
size_categories:
  - n<1K

UltraSound ToolBox (USTB) Channel Capture Collection

First frames reconstructed from the raw channel data with reconstruct.py. Top: A_cardiac/Verasonics_P2-4_parasternal_long_subject_1, A_cardiac/Verasonics_P2-4_apical_four_chamber_subject_1, C_verasonics_phantom/FI_P4_cysts_center. Bottom: B_carotid/L7_FI_carotid_cross_1, C_verasonics_phantom/L7_FI_Verasonics_CIRS, D_alpinion_phantom/Alpinion_L3-8_CPWC_hypoechoic, E_simulation/PICMUS_numerical_calib_v2. Panels are scaled to a common height per row, not to a common physical scale.

Dataset Description

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

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.

Dataset Contributor(s)

• Ole Marius Hoel Rindal omrindal@ifi.uio.no (primary contact)
• Yucel Karabiyik
• Sven Peter Nasholm
• Andreas Austeng
• University of Oslo (UiO), Department of Informatics

Dataset Creation Date

06/23/2026 (packaging date; original acquisitions/simulations were produced between 2016 and 2023).

License / Terms of Use

Creative Commons Attribution 4.0 International (CC BY 4.0). Retain attribution and identify modifications when reusing the data.

Processing the Dataset

Every acquisition is reconstructable from the file alone — all acquisition parameters live in the zea /scan and /probe groups. The acquisitions can be processed with the reconstruct.py script, as provided in the Open-H-RF GitHub repository, together with the pipeline*.yaml and parameters.yaml definitions in this folder and the zea library. The script streams the data from the Hugging Face Hub: set PATHS to the acquisition to reconstruct (default: the apical four-chamber cardiac scan above) and REFOCUS = True to also emit the REFoCUS variant. See Reconstruction Details for how each acquisition's pipeline is chosen. Each sub-dataset folder also holds a pipeline.yaml for its example acquisition, so it can be rendered with a single zea process command (see the sub-dataset cards).

Dataset Format

zea v0.1.6

Dataset Quantification

Current Open-H-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.

Reconstruction Details

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. With REFOCUS = True (off by default), reconstruct.py 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).

Citation

Please cite the UltraSound ToolBox (USTB) Channel Capture Collection, 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.