Magnetic Resonance Spectroscopy Quantification using Deep Learning
This addresses the challenge of high uncertainties in MRS quantification for biomedical research, potentially aiding clinical applications, though it appears incremental as it applies deep learning to an existing problem.
The paper tackled the problem of quantifying metabolites in magnetic resonance spectroscopy (MRS) by proposing a deep learning method using convolutional neural networks, achieving accurate estimation compared to the state-of-the-art QUEST method on 20 metabolites and macromolecules.
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.