Free breathing cardiac T2 mapping via single-shot ZOOM-MOLED and deep learning reconstruction

Motivation: Quantitative cardiac magnetic resonance (CMR) imaging has important applications in clinic. However, conventional parametric mapping methods suffer from inherent inefficiencies. Goal(s): To enhance the resolution of reconstructed images, mitigate image distortion and artifacts, and improve the signal-to-noise ratio in quantitative CMR imaging. Approach: A method was proposed which combines single-shot multiple overlapping-echo detachment (MOLED) imaging with outer volume suppression (OVS) and zonal oblique multislice (ZOOM) techniques, and deep learning reconstruction was used for image reconstruction. Results: The results of simulation, phantom, and in vivo healthy volunteer experiments show great performance of the proposed method. Impact: This study developed a cardiac T2 mapping method without requirement of breath-holding or respiratory-gating. It paves the way for real-time dynamic high-resolution cardiac T2 mapping.