Low Rank Magnetic Resonance Fingerprinting

Abstract

Purpose: Magnetic Resonance Fingerprinting (MRF) is a relatively new approach that provides quantitative MRI measures using randomized acquisition. Extraction of physical quantitative tissue parameters is performed off-line, without the need of patient presence, based on acquisition with varying parameters and a dictionary generated according to the Bloch equation simulations. MRF uses hundreds of radio frequency (RF) excitation pulses for acquisition, and therefore a high undersampling ratio in the sampling domain (k-space) is required for reasonable scanning time. This undersampling causes spatial artifacts that hamper the ability to accurately estimate the tissue's quantitative values. In this work, we introduce a new approach for quantitative MRI using MRF, called magnetic resonance Fingerprinting with LOw Rank (FLOR). Methods: We exploit the low rank property of the concatenated temporal imaging contrasts, on top of the fact that the MRF signal is sparsely represented 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. Results: Experimental results consist of retrospective sampling, that allows comparison to a well defined reference, and prospective sampling that shows the performance of FLOR for a real-data sampling scenario. Both experiments demonstrate improved parameter accuracy compared to other compressed-sensing and low-rank based methods for MRF at 5% and 9% sampling ratios, for the retrospective and prospective experiments, respectively. Conclusions: We have shown through retrospective and prospective experiments that by exploiting the low rank nature of the MRF signal, FLOR recovers the MRF temporal undersampled images and provides more accurate parameter maps compared to previous iterative methods.

arXiv comment11 pages, 11 figures

References in corpus (4)

• Low Rank Alternating Direction Method of Multipliers Reconstruction for MR Fingerprinting
• Compressed sensing for longitudinal MRI: An adaptive-weighted approach
• Reference-Based MRI
• PEAR: PEriodic And fixed Rank separation for fast fMRI

Cited by in corpus (11)

• Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning
• Low Rank Alternating Direction Method of Multipliers Reconstruction for MR Fingerprinting
• Coupled Dictionary Learning for Multi-contrast MRI Reconstruction
• Automated Design of Pulse Sequences for Magnetic Resonance Fingerprinting using Physics-Inspired Optimization
• HYDRA: Hybrid Deep Magnetic Resonance Fingerprinting
• Cramér-Rao bound-informed training of neural networks for quantitative MRI
• Spatially Regularized Parametric Map Reconstruction for Fast Magnetic Resonance Fingerprinting
• Greedy Approximate Projection for Magnetic Resonance Fingerprinting with Partial Volumes
• Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders
• Optimization with learning-informed differential equation constraints and its applications
• Quantitative Magnetic Resonance Imaging: From Fingerprinting to Integrated Physics-Based Models