Rapid Motion Estimation and Motion-Corrected End-to-End Deep Learning Reconstruction for 1 Heartbeat CINE

Motivation: Cardiac CINE provides dynamic images of the heart for morphology and function assessment. Single-heartbeat CINE enables faster acquisition times and the study of heart rate variations, but conventional reconstruction methods incur significant computational cost. Goal(s): This study aims to speed up single-heartbeat CINE reconstruction by using deep learning reconstruction. Approach: We propose a novel, rapid, end-to-end deep learning pipeline for motion estimation and motion-corrected single-heartbeat CINE reconstruction with golden-angle radial acquisition. Results: The network reconstructs each CINE slice in ~40 seconds (400 times faster than state-of-the-art), with comparable image quality, achieving SSIM values ranging from 0.75 to 0.84 across cardiac phases and slices. Impact: The proposed approach enables reconstruction of single-heartbeat golden-angle radial CINE acquisition in ~40 seconds, making it clinically feasible. Single-heartbeat CINE could reduce scan times, achieve acquisitions of multiple slices in a single breath-hold and be robust to heart rate variations.