1 research outputs found
Qunatification of Metabolites in MR Spectroscopic Imaging using Machine Learning
Magnetic Resonance Spectroscopic Imaging (MRSI) is a clinical imaging
modality for measuring tissue metabolite levels in-vivo. An accurate estimation
of spectral parameters allows for better assessment of spectral quality and
metabolite concentration levels. The current gold standard quantification
method is the LCModel - a commercial fitting tool. However, this fails for
spectra having poor signal-to-noise ratio (SNR) or a large number of artifacts.
This paper introduces a framework based on random forest regression for
accurate estimation of the output parameters of a model based analysis of MR
spectroscopy data. The goal of our proposed framework is to learn the spectral
features from a training set comprising of different variations of both
simulated and in-vivo brain spectra and then use this learning for the
subsequent metabolite quantification. Experiments involve training and testing
on simulated and in-vivo human brain spectra. We estimate parameters such as
concentration of metabolites and compare our results with that from the
LCModel