%0 Generic
%A Zorgdrager, Rienk
%A Blanken, Nathan
%A Wolterink, Jelmer
%A Versluis, Michel
%A Lajoinie, Guillaume
%D 2025
%T Dataset underlying the study: Waveform-Specific Performance of Deep Learning-Based Super-Resolution for Ultrasound Contrast Imaging
%U 
%R 10.4121/cc1c073d-23bf-4a1e-b9f4-9f878c95722d.v1
%K Chirp
%K Deep Learning
%K Flow Imaging
%K Microbubbles
%K Super-resolution
%K Ultrasound Contrast Imaging
%X <p>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). <strong>Contents:</strong>&nbsp;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. <strong>Objective:</strong>&nbsp;To investigate the effect of transmit waveforms on the performance of deep learning-based approaches for localizing microbubbles in radiofrequency signals. <strong>Type of research:</strong>&nbsp;Fundamental, Physics, Biomedical. <strong>Method of data collection:</strong>&nbsp;In-silico and in-vitro. <strong>Type of data:</strong>&nbsp;RF signals (<code style="color: rgb(199, 37, 78); background-color: rgb(249, 242, 244);">.mat</code>&nbsp;and&nbsp;<code style="color: rgb(199, 37, 78); background-color: rgb(249, 242, 244);">.txt</code>), super-resolved RF signals (<code style="color: rgb(199, 37, 78); background-color: rgb(249, 242, 244);">.txt</code>&nbsp;and&nbsp;<code style="color: rgb(199, 37, 78); background-color: rgb(249, 242, 244);">.npy</code>), weights and biases (<code style="color: rgb(199, 37, 78); background-color: rgb(249, 242, 244);">.txt</code>), images generated with the (super-resolved) RF signals (<code style="color: rgb(199, 37, 78); background-color: rgb(249, 242, 244);">.mat</code>), videos generated with the (super-resolved) RF signals (<code style="color: rgb(199, 37, 78); background-color: rgb(249, 242, 244);">.mp4</code>), python environments (<code style="color: rgb(199, 37, 78); background-color: rgb(249, 242, 244);">.yaml</code>), microbubble size distributions (<code style="color: rgb(199, 37, 78); background-color: rgb(249, 242, 244);">.fig</code>&nbsp;and&nbsp;<code style="color: rgb(199, 37, 78); background-color: rgb(249, 242, 244);">.png</code>). The code used for this work is available at&nbsp;<a href="https://github.com/MIAGroupUT/super-resolution-waveforms" target="_blank" style="background-color: initial; color: rgb(160, 170, 191);">https://github.com/MIAGroupUT/super-resolution-waveforms</a>.&nbsp;</p>
%I 4TU.ResearchData