LOW RANK AND SPATIAL REGULARIZATION MODEL FOR MAGNETIC RESONANCE FINGERPRINTING

摘要

Systems and methods are provided for iterative reconstruction of a magnetic resonance image using Magnetic Resonance Fingerprinting (MRF). An image series is estimated according to the following three steps: a gradient step to improve data consistency, fingerprint matching, and a spatial regularization. Singular Value Decomposition (SVD) compression may be used along the time dimension to accelerate both the matching and the spatial regularization that operates in the compressed domain as well as to enforce low-rank regularization.