Deep Learning Reconstruction for Free-Breathing Radial Cine Imaging

Motivation: We aim to introduce a cardiac cine imaging protocol to address the issues of motion susceptibility and robustness in the previous techniques. Goal(s): Our objective is to demonstrate an accelerated acquisition and high-quality reconstruction framework based on free-breathing radial cardiac cine imaging that shows enhanced patient comfort and robustness against respiratory motion. Approach: We synergistically leverage a raw k-space preprocessing module, region optimized coil compression, and deep learning reconstruction based on memory efficient unrolled neural networks. Results: Our experiments indicate that the proposed framework achieves high reconstruction quality at large acceleration factors (e.g., 8x), in terms of spatial and temporal accuracy. Impact: Conventional cardiac protocols use Cartesian k-space sampling and are susceptible to motion artifacts. We provide an acquisition and reconstruction framework based on a free-breathing protocol and deep learning reconstruction for enhanced patient comfort and robustness against motion artifacts.