Qunatification of Metabolites in MR Spectroscopic Imaging using Machine Learning

TL;DR

This paper presents a machine learning framework using random forest regression to improve metabolite quantification in MR Spectroscopic Imaging, especially for spectra with poor SNR or artifacts, outperforming traditional methods.

Contribution

It introduces a novel machine learning approach for spectral parameter estimation in MRSI, addressing limitations of existing methods like LCModel.

Findings

The framework accurately estimates metabolite concentrations.

It performs better than LCModel on noisy and artifact-laden spectra.

Validated on both simulated and real in-vivo data.

Abstract

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.