Magnetic Resonance Spectroscopy Quantification using Deep Learning

TL;DR

This paper introduces a deep learning-based regression framework using CNNs for more accurate and robust quantification of metabolites in magnetic resonance spectroscopy, addressing challenges like low SNR and spectral overlaps.

Contribution

It presents a novel CNN-based method trained on simulated data to improve metabolite quantification accuracy over traditional approaches.

Findings

Outperforms QUEST in metabolite concentration estimation.

Effective across various SNR levels and spectral variations.

Accurately quantifies 20 metabolites and macromolecules.

Abstract

Magnetic resonance spectroscopy (MRS) is an important technique in biomedical research and it has the unique capability to give a non-invasive access to the biochemical content (metabolites) of scanned organs. In the literature, the quantification (the extraction of the potential biomarkers from the MRS signals) involves the resolution of an inverse problem based on a parametric model of the metabolite signal. However, poor signal-to-noise ratio (SNR), presence of the macromolecule signal or high correlation between metabolite spectral patterns can cause high uncertainties for most of the metabolites, which is one of the main reasons that prevents use of MRS in clinical routine. In this paper, quantification of metabolites in MR Spectroscopic imaging using deep learning is proposed. A regression framework based on the Convolutional Neural Networks (CNN) is introduced for an accurate estimation of spectral parameters. The proposed model learns the spectral features from a large-scale simulated data set with different variations of human brain spectra and SNRs. Experimental results demonstrate the accuracy of the proposed method, compared to state of the art standard quantification method (QUEST), on concentration of 20 metabolites and the macromolecule.