Magnetic Resonance Fingerprinting by exploiting Low Rank

Gal Mazor, Lior Weizman, Assaf Tal, Yonina C. Eldar

Research output: Contribution to conferencePosterpeer-review

Abstract

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, 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 for reasonable scanning time. This under-sampling 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). 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. Experiments on real MRI data, acquired using a spirally-sampled MRF FISP sequence, demonstrate better resolution compared to other compressed-sensing based methods for MRF at 5% sampling ratio.
Original languageEnglish
Number of pages2
StatePublished - Jun 2017
Externally publishedYes
Event6th Signal Processing with Adaptive Sparse Structured Representations workshop 2017 - Lisbon, Portugal, Lisbon, Portugal
Duration: 5 Jun 20178 Jun 2017

Conference

Conference6th Signal Processing with Adaptive Sparse Structured Representations workshop 2017
Abbreviated titleSPARS 2017
Country/TerritoryPortugal
CityLisbon
Period5/06/178/06/17

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