# Magnetic Resonance Spectroscopy Quantification using Deep Learning

arXiv: https://arxiv.org/abs/1806.07237 · HTML: https://mlsanity.com/paper/1806.07237/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2018.

## Built on by (most cited first, 8 of 8)

- [Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal](https://mlsanity.com/paper/2306.09681/) (2023)
- [Deep Learning, Natural Language Processing, and Explainable Artificial Intelligence in the Biomedical Domain](https://mlsanity.com/paper/2202.12678/) (2022)
- [WALINET: A water and lipid identification convolutional Neural Network for nuisance signal removal in 1H MR Spectroscopic Imaging](https://mlsanity.com/paper/2410.00746/) (2024)
- [Preserved Edge Convolutional Neural Network for Sensitivity Enhancement of Deuterium Metabolic Imaging (DMI)](https://mlsanity.com/paper/2309.04100/) (2023)
- [Multiple Instance Learning for Brain Tumor Detection from Magnetic Resonance Spectroscopy Data](https://mlsanity.com/paper/2112.08845/) (2021)
- [Development of MR spectral analysis method robust against static magnetic field inhomogeneity](https://mlsanity.com/paper/2506.20897/) (2025)
- [The Sim-to-Real Gap in MRS Quantification: A Systematic Deep Learning Validation for GABA](https://mlsanity.com/paper/2602.20289/) (2026)
- [HyperFitS -- Hypernetwork Fitting Spectra for metabolic quantification of 1H MR spectroscopic imaging](https://mlsanity.com/paper/2604.03150/) (2026)

## Abstract (arXiv)

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.