Cluster-Based Low-Rank Regularization for Cardiac Rosette MR Fingerprinting

Motivation: Breathholds limit the amount of data which can be acquired in cardiac MRF, which can impact the precision of fat/water separated T1, T2, and T2* maps. Goal(s): We developed a regularization method to reconstruct accurate maps from multi-echo cMRF data without introducing blurring into the resulting tissue property maps. Approach: A k-means cluster-based approach is used to group the signal evolutions during reconstruction and a low-rank constraint is applied to each cluster. We compared our method to existing approaches in 23 healthy volunteers. Results: This approach can be used to generate accurate myocardial T1, T2, and T2* maps using rosette MRF data. Impact: Traditional cardiac MRF reconstructions can fail when working with multi-echo rosette MRF data due to insufficient sampling. We developed a reconstruction method which enables T1, T2, and T2* maps to be collected in a single breathhold without compromising accuracy.