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

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.