Abstract #2095

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

Johnathan Le^1,2,3, Ye Tian^2,3,4, Jason Mendes^2,3, Mark Ibrahim⁵, Brent Wilson⁵, Edward DiBella^1,2,3, and Ganesh Adluru^1,2,3

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.

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