Highly accelerated CINE cardiac MR deep-learning image reconstruction in combination with compressed sensing: A quantitative evaluation.

Motivation: Deep-learning image reconstruction is promising for improving image quality and may enable higher acceleration for cardiac CINE MR. Goal(s): To investigate deep-learning-based image reconstruction for accelerating cardiac CINE MRI over compressed sensing (CS) reconstructions without affecting quantitative measures of ejection fraction. Approach: Cardiac CINE MR with CS factors 2-5 were acquired in 15 volunteers and reconstructed with one standard and two AI methods. Ejection fractions were calculated, and a subset of images were graded for image quality. Results: CS-factors did not materially affect ejection fractions (left-ventricle p=0.969, right-ventricle p=0.998). Blurring (p<0.01) and perceived SNR (p<0.01) were improved by AI reconstruction at high acceleration. Impact: Deep learning MR reconstruction reduces penalties from high acceleration in cardiac MRI CINE imaging, allowing for shorter breath-holds, shorter exams, non-compromised image quality, and preserved quantitative measurements.