Dataset underlying the study: Waveform-Specific Performance of Deep Learning-Based Super-Resolution for Ultrasound Contrast Imaging

doi:10.4121/cc1c073d-23bf-4a1e-b9f4-9f878c95722d.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/cc1c073d-23bf-4a1e-b9f4-9f878c95722d
Datacite citation style:
Zorgdrager, Rienk; Blanken, Nathan; Wolterink, Jelmer; Versluis, Michel; Lajoinie, Guillaume (2025): Dataset underlying the study: Waveform-Specific Performance of Deep Learning-Based Super-Resolution for Ultrasound Contrast Imaging. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/cc1c073d-23bf-4a1e-b9f4-9f878c95722d.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset

This dataset contains the data used for the study ‘Waveform-Specific Performance of Deep Learning-Based Super-Resolution for Ultrasound Contrast Imaging’ (link will be added after publication). Contents: It consists of radiofrequency (RF) signals acquired during simulations and experiments, weights and biases of networks trained, and images reconstructed using delay-and-sum (DAS) beamforming. In addition to that, it also contains environments and packages used to process the data. Objective: To investigate the effect of transmit waveforms on the performance of deep learning-based approaches for localizing microbubbles in radiofrequency signals. Type of research: Fundamental, Physics, Biomedical. Method of data collection: In-silico and in-vitro. Type of data: RF signals (.mat and .txt), super-resolved RF signals (.txt and .npy), weights and biases (.txt), images generated with the (super-resolved) RF signals (.mat), videos generated with the (super-resolved) RF signals (.mp4), python environments (.yaml), microbubble size distributions (.fig and .png). The code used for this work is available at https://github.com/MIAGroupUT/super-resolution-waveforms. 

history
  • 2025-01-21 first online, published, posted
publisher
4TU.ResearchData
format
Text data/.txt, NumPy data/.npy, MATLAB data/.mat, videos/.mp4, environments/.yaml, figures/.fig and .png.
funding
  • Super-FALCON: Super-resolution, ultrafast and deeply-learned contrast ultrasound imaging of the vascular tree. (grant code 101076844) [more info...] European Research Council (ERC)
  • 4TU Program Precision Medicine
organizations
University of Twente, Faculty of Science and Technology (TNW), Physics of Fluids group

DATA

files (4)