Keywords: MR Fingerprinting, MR Fingerprinting, Sparse & Low-Rank Models, Heart, Quantitative Imaging
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.
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