Deep learning reconstruction improved precision of myocardial T1 and T2 mapping: initial phantom and in-vivo evaluation

Motivation: Multiparametric mapping has been widely used for the characterization of myocardial tissues. Quantitative assessment of myocardial T1 and T2 values allows objective evaluation of abnormalities. However, the sensitivity of T1 and T2 in detecting abnormality is limited by the precision of their measurements in both acquisition and reconstruction. Goal(s): To evaluate the potential of DLR-assisted reconstruction in improving the precision of T1 and T2 maps. Approach: Phantom and three in-vivo studies at a 3T system. Results: Standard deviation was reduced in both phantom and in-vivo T1 and T2 maps using DLR. The average T1 and T2 values were no different. Impact: Applying deep-learning assisted reconstruction improves precision in T1 and T2 mapping. The ability to reduce the standard deviation has the potential to increase the statistical power of detecting subtle abnormalities due to diffuse fibrosis or edema.