Fast Group Matching Method based on Singular Value Decomposition Decomposition for Magnetic Resonance Fingerprinting
HUANG Min
FAN Lingling
GAN Bowen
CHEN Junbo
Abstract：The traditional MRI can only acquire a weighted map in a single scan.Magnetic resonance fingerprinting is a new technique requiring a novel data processing method.It can simultaneously generate quantitative maps of different tissue parameters, such as the relaxation time T1, T2 and proton density.The direct matching algorithm is to do inner-product operation between the observed signal and the signals of each entry in the dictionary.Though the direct matching algorithm has shown the MR parameters of interest accurately, matching time is very long.We put forward a more efficient method called fast group matching method based on the singular value decomposition (SVD).Two dictionaries with different sets of characteristic parameters of T1 and T2 were simulated based on the brain and the model.Then a random dictionary entry element that is normalized as an initial signal was choosen.According to the correlation coefficient from inner-product between the signal and all dictionary entries, dictionary could be assigned to different groups.SVD was made on each group to get feature information, which compresses the size of dictionary and speeding up the matching algorithm.The results of tests on the brain and model data show that multi tissue parametric maps can acquire accurately and efficiently.
Keywords：Magnetic resonance fingerprintingSingular value decompositionGroup matchingDictionaryParameter map
Publication Date：2017-01-01
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：5( 112-115,120 )