EPFL Ultrafast Ultrasound Datasets

1.  Deep Learning-based Inpainting for Sparse Arrays in Ultrafast Ultrasound Imaging: Dataset

Overview

This website provides access to the dataset associated with the publication:

R. Viñals, J.-P. Thiran — Deep Learning-based Inpainting for Sparse Arrays in Ultrafast Ultrasound Imaging, IEEE Transactions on Computational Imaging, 2025.

This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/. Please cite this paper when using the dataset. 

The EPFL Ultrafast Ultrasound Dataset is a large-scale collection of in vivo and in vitro ultrafast ultrasound acquisitions designed for research on deep learning, image reconstruction, beamforming, and sparse-array imaging. It includes over 20,000 in vivo acquisitions, detailed metadata, and complete acquisition settings.

Download the Dataset

For stable and reproducible access, we recommend downloading via the direct links below or using the S3 interface (instructions provided further down).

Core Files

In Vivo Datasets

Access via S3 (recommended for large transfers)

The dataset is hosted on an S3-compatible endpoint and can be accessed using rclone.

  1. Create an rclone remote
    • Add to ~/.config/rclone/rclone.conf:
      • [datasets_epfl]
      • type = s3
      • provider = Other
      • endpoint = https://datasets.epfl.ch
      • region = us-east-1
      • env_auth = false
      • access_key_id =
      • secret_access_key =
    • Or create it automatically: rclone config create datasets_epfl s3 provider Other endpoint https://datasets.epfl.ch region us-east-1
  2. List all files: rclone ls datasets_epfl:epfl_ultrafast_ultrasound
  3. Download a file:rclone copy datasets_epfl:epfl_ultrafast_ultrasound/volunteer_002.zip . -P
  4. Download the full dataset: rclone sync datasets_epfl:epfl_ultrafast_ultrasound ./epfl_ultrafast_ultrasound -P

Dataset Description

  • In Vivo Data

    • Total number of acquisitions: 20,000
    • Participants: 9 volunteers
    • Format: One .zip archive per volunteer (multiple archives for Volunteer 1)
    • Acquisition Distribution by Body Region
      • Abdomen: 6,599
      • Neck: 3,294
      • Breast: 3,291
      • Lower limbs: 2,616
      • Upper limbs: 2,110
      • Back: 2,090
    • Additional (unused in publications):
      • Shoulders: 123 (volunteer_001)
      • Arms: 19 (invivo_04451.npz → invivo_0469.npz)
    • File Naming: invivo_00000.npz → invivo_20141.npz

  • In Vitro Data

    • Total number of acquisitions: 2,179
    • Phantom: CIRS Model 054G
    • File Naming: invitro_00000.npz → invitro_02178.npy

Acquisition & Beamforming Parameters

Provided in the settings folder:

File Description
beamforming_settings.yaml Probe, acquisition and beamforming parameters (SI units)
steering_angles.npy 87 steering angles (radians)
time_axis.npy Global RF time axis
time_axis_per_angle.npy Per-angle time axes
sequence_verasonics_ge9ld_87pws.mat Original Verasonics sequence file

File Format

All acquisitions are stored as NumPy arrays: import numpy as np data = np.load("file.npz")

Publications Using This Dataset

Selected In Vivo Acquisitions by Paper

Year Paper Title Acquisitions Used
2025 Deep Learning-based Inpainting for Sparse Arrays in Ultrafast Ultrasound Imaging (IEEE Transactions on Computational Imaging, 2025) invivo_18198 (V8, carotid), invivo_16874 (V8, abdomen)
2025 Multi-Plane Wave Signal Inpainting with CNNs: A Framework for Reducing RF Data Volume in Ultrafast Ultrasound (IEEE IUS 2025) invivo_14592 (V5, abdomen), invivo_14888 (V5, abdomen)
2024 Enhancement of Ultrafast Ultrasound Images: a Performance Comparison Between CNN Trained with RF or IQ Images (UFFC-JS 2024) invivo_18296 (V8, carotid), invivo_14786 (V5, abdomen)
2024 Sequential CNN-Based Enhancement of Ultrafast Ultrasound Imaging for Sparse Arrays (EUSIPCO 2024) invivo_18171 (V8, carotid)
2023 Quality Enhancement of Ultrafast Ultrasound Images with Deep Networks and Transfer Learning (IEEE IUS 2023) invivo_14965 (V5, carotid), invivo_15002 (V5, carotid)
2023 A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning (Journal of Imaging, 2023) invivo_17688 (V8, back), invivo_15026 (V5, carotid)
 

In Vitro Acquisitions Used in Publications

Year Paper Title Acquisitions Used
2025 Deep Learning-based Inpainting for Sparse Arrays in Ultrafast Ultrasound Imaging (IEEE Transactions on Computational Imaging, 2025) invitro_02162.npz
2024 Enhancement of Ultrafast Ultrasound Images: a Performance Comparison Between CNN Trained with RF or IQ Images (UFFC-JS 2024) invitro_02162.npz
2023 A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning (Journal of Imaging, 2023) invitro_02162.npz

 

Training / Validation / Test Splits

  • Default split (used in most papers):
    • Training: 1, 2, 3, 6, 7, 9
    • Validation: 4
    • Test: 5, 8
  • Split used in IUS 2023 (Quality Enhancement of Ultrafast Ultrasound Images with Deep Networks and Transfer Learning)
    • Training: 1, 2, 3, 4, 6, 7, 9
    • Validation: 8
    • Test: 5

License

This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0): https://creativecommons.org/licenses/by/4.0/

Please cite the corresponding publication when using this dataset.

Contact

For questions or issues: