Abstract #2216

Rapid myocardial perfusion MRI reconstruction using deep learning networks

Eric Kenneth Gibbons^1,2, Ye Tian³, Qi Huang⁴, Akshay Chaudhari⁵, and Edward DiBella^2,4

¹Electrical and Computer Engineering, Weber State University, Ogden, UT, United States, ²Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, United States, ³Physics, University of Utah, Salt Lake City, UT, United States, ⁴Biomedical Engineering, University of Utah, Salt Lake City, UT, United States, ⁵Radiology, Stanford University, Stanford, CA, United States

Current acquisition strategies in cardiac perfusion MRI rely on non-uniform sampling that is highly undersampled in spatial and temporal domains. While iterative reconstruction methods are able to reconstruct such data reasonably well, reconstruction speeds are prohibitively long. This abstract applies novel deep learning approaches to accelerate reconstruction speeds relative to iterative algorithms with comparable image quality. Validation is performed through the calculation of a perfusion index.

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