Abstract #0805

Rapid Motion Correction with Deep Learning for First-Pass Cardiac Perfusion MRI

Lexiaozi Fan^1,2, Huili Yang^1,2, Li-Yueh Hsu³, Aggelos K Katsaggelos^1,4,5, Bradley D Allen¹, Daniel C Lee⁶, and Daniel Kim^1,2

¹Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States, ²Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States, ³Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD, United States, ⁴Department of Electrical Engineering, Northwestern University, Evanston, IL, United States, ⁵Department of Computer Science, Northwestern University, Evanston, IL, United States, ⁶Division of Cardiology, Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States

Synopsis

Motion correction (MoCo) is an important pre-processing step for pixel-by-pixel myocardial blood flow (MBF) quantification from cardiac perfusion MRI. It may also improve throughput of visual evaluation of perfusion images. One commonly used method for MoCo is optical flow (OF), which requires a moderate level of computational demand. In this study, we sought to perform rapid MoCo of respiratory motion on cardiac perfusion images using deep learning (DL). Our results show that the proposed DL MoCo performs 418-times faster than the reference OF approach without loss in accuracy.

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