# Low Rank Magnetic Resonance Fingerprinting

## TL;DR

This paper introduces FLOR, a novel low-rank based method for Magnetic Resonance Fingerprinting that improves quantitative tissue parameter estimation accuracy in highly undersampled MRI data.

## Contribution

The paper presents a new low-rank iterative approach for MRF that leverages the low rank property of the signal to enhance parameter map accuracy over existing methods.

## Key findings

- FLOR outperforms other methods at 5% and 9% sampling ratios.
- Experimental results show improved parameter accuracy.
- Both retrospective and prospective experiments validate FLOR's effectiveness.

## 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.