Deep learning-based non-rigid motion-corrected reconstruction for whole-heart multi-dimensional joint T1/T2 imaging

Myocardial T1 and T2 mapping play an important role in the assessment of cardiovascular disease. 3D whole-heart joint T1/T2 water/fat mapping approaches have been recently proposed, however they require long reconstruction times. Recently a Machine learning based reconstruction was proposed for joint motion correction and motion corrected image reconstruction of undersampled free-breathing single contrast 3D coronary MR angiography. Here, we extend this approach for non-rigid motion-corrected reconstructions for multi-contrast data for joint T1/T2 mapping. The proposed approach achieves good agreement with reference techniques and comparable image quality to state-of-the-art methods albeit in 1200 times shorter reconstruction times.