Rapid 2D Myocardial Late Gadolinium Enhancement Imaging with Resolution Enhancement Generative Adversarial Inline Neural Network

Motivation: 2D high-resolution late gadolinium enhancement (LGE) imaging can benefit from shorter scan times. Current acceleration techniques lead large signal-to-noise (SNR) penalties, reducing diagnostic quality while longer breath-holds increases scan time and makes the imaging susceptible to artifacts. Goal(s): To assess the feasibility of a rapid 2D LGE imaging using Resolution Enhancement Generative Adversarial Inline Neural Network (REGAIN). Approach: Images were acquired with 3.3 and 5.7-fold accelerations, reconstructed using REGAIN, and compared with 1.8-fold GRAPPA acceleration. Results: REGAIN successfully improved visual image sharpness in LGE images acquired with 3.3-fold (~6-second) and 5.7-fold (~10-second) accelerations. Image quality was comparable to 1.8-fold (~16-second) GRAPPA acceleration. Impact: REGAIN enables accelerated LGE imaging with significantly reduced breath-hold duration.