Q-MRS: A Deep Learning Framework for Quantitative Magnetic Resonance Spectra Analysis

TL;DR

This paper introduces Q-MRS, a deep learning framework utilizing transfer learning to improve the accuracy and reproducibility of magnetic resonance spectroscopy data analysis, addressing limitations of existing methods.

Contribution

It presents a novel deep learning approach with transfer learning for MRS quantification, enhancing data analysis accuracy and robustness.

Findings

Effective transfer learning improves spectral quantification.

Framework performs well on real in vivo datasets.

Advances MRS analysis with deep learning techniques.

Abstract

Magnetic resonance spectroscopy (MRS) is an established technique for studying tissue metabolism, particularly in central nervous system disorders. While powerful and versatile, MRS is often limited by challenges associated with data quality, processing, and quantification. Existing MRS quantification methods face difficulties in balancing model complexity and reproducibility during spectral modeling, often falling into the trap of either oversimplification or over-parameterization. To address these limitations, this study introduces a deep learning (DL) framework that employs transfer learning, in which the model is pre-trained on simulated datasets before it undergoes fine-tuning on in vivo data. The proposed framework showed promising performance when applied to the Philips dataset from the BIG GABA repository and represents an exciting advancement in MRS data analysis.