Patient‐specific automated multi‐class anatomical motion tracking on real‐time cine MR images using deep learning techniques

Abstract

Real‐time multi‐class motion tracking on cine images during magnetic resonance‐guided radiation therapy (MRgRT) would enable instant monitoring of anatomical structures, facilitating dynamic beam control to minimize overdose to normal tissues and maximize tumor dose. However, conventional segmentation strategies incur significant time delays, making their clinical application impractical. Emerging deep learning (DL) technologies can provide a path to address this clinical challenge.