Meeting Banner
Abstract #2095

Deep Learning for Radial Myocardial Perfusion Reconstruction using 3D residual booster U-Nets

Johnathan Le1,2,3, Ye Tian2,3,4, Jason Mendes2,3, Mark Ibrahim5, Brent Wilson5, Edward DiBella1,2,3, and Ganesh Adluru1,2,3
1Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States, 2Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, United States, 3Utah Center for Advanced Imaging Research (UCAIR), University of Utah, Salt Lake City, UT, United States, 4Department of Physics, University of Utah, Salt Lake City, UT, United States, 5Department of Cardiology, University of Utah, Salt Lake City, UT, United States

Although dynamic contrast enhanced (DCE) MRI has been successfully applied for characterizing coronary artery diseases, an acquisition scheme limited to 2-4 short axis slices restricts coverage of the left ventricle. Radial simultaneous multi-slice (SMS) has been shown to improve DCE cardiac perfusion by providing complete coverage of the left ventricle but also requires an increase in reconstruction time. Here we propose using a 3D residual booster U-Net to improve reconstruction time of spatio-temporal constrained reconstruction methods for radial SMS datasets. Results demonstrate promising improvements with a speed up in reconstruction by a factor of ~200.

This abstract and the presentation materials are available to 2020 meeting attendees and eLibrary customers only; a login is required.

Join Here