Meeting Banner
Abstract #0805

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

Lexiaozi Fan1,2, Huili Yang1,2, Li-Yueh Hsu3, Aggelos K Katsaggelos1,4,5, Bradley D Allen1, Daniel C Lee6, and Daniel Kim1,2
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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here