title: Low rank magnetic resonance fingerprinting.
abstract: Magnetic Resonance Fingerprinting (MRF) is a relatively new approach that provides quantitative MRI using randomized acquisition. Extraction of physical quantitative tissue values is preformed off-line, based on acquisition with varying parameters and a dictionary generated according to the Bloch equations. MRF uses hundreds of radio frequency (RF) excitation pulses for acquisition, and therefore high under-sampling ratio in the sampling domain (k-space) is required. This under-sampling causes spatial artifacts that hamper the ability to accurately estimate the quantitative tissue values. In this work, we introduce a new approach for quantitative MRI using MRF, called Low Rank MRF. We exploit the low rank property of the temporal domain, on top of the well-known sparsity of the MRF signal in the generated dictionary domain. We present an iterative scheme that consists of a gradient step followed by a low rank projection using the singular value decomposition. Experiments on real MRI data demonstrate superior results compared to conventional implementation of compressed sensing for MRF at 15% sampling ratio.
authors.forename: Gal
authors.initials: G
authors.lastname: Mazor
authors.forename: Lior
authors.initials: L
authors.lastname: Weizman
authors.forename: Assaf
authors.initials: A
authors.lastname: Tal
authors.forename: Yonina C
authors.initials: YC
authors.lastname: Eldar
mesh_terms: D000465:Algorithms; D016477:Artifacts; D006801:Humans; D008279:Magnetic Resonance Imaging; D009682:Magnetic Resonance Spectroscopy; D008962:Models, Theoretical