A Study on the Feasibility and Accuracy of Rapid T1 Mapping Utilizing Deep Learning Techniques in Cardiac Magnetic Resonance Imaging

Motivation: To overcome limitations of prolonged acquisition and breath-hold(BH) times in Cardiac Magnetic Resonance Imaging (CMR), specifically MOLLI sequence, enhance patient comfort and compliance. Goal(s): Aimed to validate MyoMapNet sequence with inline reconstruction against the standard MOLLI protocol, focusing on image quality and T1 measurement accuracy, reducing scan time and BH durations. Approach: 20 subjects were imaged using two sequences. Image quality was assessed via edge sharpness and signal intensity ratios,T1 accuracy was determined through myocardial segment analysis. Results: MyoMapNet achieved comparable image quality and T1 accuracy to MOLLI with shorter acquisition times, demonstrating no significant difference in myocardial and blood pool T1 values. Impact: MyoMapNet offers a rapid and reliable alternative for myocardial T1 mapping, reducing scan time and heart rate dependence, which can improve patient throughput and comfort in clinical CMR workflows. Future studies will expand to post-contrast T1 values and ECVs.