Physics-informed deep learning approach to quantification of human brain metabolites from magnetic resonance spectroscopy data. (May 2023)

• Record Type: Journal Article
• Authors: Shamaei, Amirmohammad; Starcukova, Jana; Starcuk, Zenon
• Is Part Of: Computers in biology and medicine. Volume 158(2023)
• Publication Date: 2023-05
• Subjects: MR spectroscopy -- Inverse problem -- Deep learning -- Machine learning -- Convolutional neural network -- Metabolite quantification; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106837
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Purpose: While the recommended analysis method for magnetic resonance spectroscopy data is linear combination model (LCM) fitting, the supervised deep learning (DL) approach for quantification of MR spectroscopy (MRS) and MR spectroscopic imaging (MRSI) data recently showed encouraging results; however, supervised learning requires ground truth fitted spectra, which is not practical. Moreover, this work investigates the feasibility and efficiency of the LCM-based self-supervised DL method for the analysis of MRS data. Method: We present a novel DL-based method for the quantification of relative metabolite concentrations, using quantum-mechanics simulated metabolite responses and neural networks. We trained, validated, and evaluated the proposed networks with simulated and publicly accessible in-vivo human brain MRS data and compared the performance with traditional methods. A novel adaptive macromolecule fitting algorithm is included. We investigated the performance of the proposed methods in a Monte Carlo (MC) study. Result: The validation using low-SNR simulated data demonstrated that the proposed methods could perform quantification comparably to other methods. The applicability of the proposed method for the quantification of in-vivo MRS data was demonstrated. Our proposed networks have the potential to reduce computation time significantly. Conclusion: The proposed model-constrained deep neural networks trained in a self-supervised manner can offer fast and efficient quantification of MRS and MRSI data. Our proposed method has the potential to facilitate clinical practice by enabling faster processing of large datasets such as high-resolution MRSI datasets, which may have thousands of spectra. Highlights: A novel self-supervised deep learning method for quantifying metabolite concentrations in MR spectroscopy signals. Providing a unique opportunity to quantify complex-valued MRS data in the time domain. Faster MR spectroscopy quantification with comparable accuracy to traditional methods. Investigating the impacts of the dataset size and neural network design on our proposed model.