Deep Learning with Spatio-Channel Regularization for Accelerated Cardiac Cine

Motivation: Evaluation of cardiac function with cine imaging remains long and requires repeated breath-holds that are sometimes corrupted with artifacts if patients have non-sinus rhythm or difficulty in breath-holding. Goal(s): To develop a deep learning method with spatio-channel regularization with multi-channel k-space reconstruction for accelerated cine imaging. Approach: Coil-self consistency based deep learning (DL) was implemented with 3D regularization across spatial and channel dimensions in contrast to single coil-combined image used in sensitivity encoding (SENSE). Results: Our approach at 5-fold acceleration showed quantitative improvements over SENSE-based DL on retrospectively accelerated data and showed good agreement with left ventricular (LV) measurements on prospectively accelerated data. Impact: The spatio-channel regularized DL reconstruction shortens the scan time by a factor of 5, leading to fewer breath-holds and 2–3-minute scans. This can greatly benefit patients struggling with breath-holding and accelerate the overall scan time.