Data-consistent super resolution for 3D whole-heart MRI using a motion-corrected deep-learning reconstruction framework

Motivation: Whole-heart CMR with high isotropic spatial resolution involves long and unpredictable scan times. Goal(s): To propose and validate a super-resolution motion-corrected reconstruction framework to enable accelerated high-resolution whole-heart CMR from lower-resolution acquisitions. Approach: Low resolution was treated as a k-space down-sampling problem, enabling the adaptation of an end-to-end motion-corrected iterative deep-learning network reconstruction, previously demonstrated for undersampled whole-heart CMRA. Results: High-resolution whole-heart images (1.5×1.5×1.5 mm3) were obtained from prospective low-resolution data (1.5×6×6 mm3) using the proposed Super-MoCo-MoDL framework, with comparable image quality to a high-resolution acquisition. Scan times decreased from ~3.2 to ~1.2 minutes and reconstruction times were clinically feasible, at ~30 seconds. Impact: The proposed Super-MoCo-MoDL framework enables data-consistent 3D whole-heart image reconstruction at high isotropic resolution from lower-resolution anisotropic scans. It has the potential to either accelerate whole-heart CMR, increase the feasibility of high-resolution clinical scanning, or a combination of the two.