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Physics-informed deep learning approach to quantification of human brain metabolites from magnetic resonance spectroscopy data
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SYSNO ASEP 0570880 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Physics-informed deep learning approach to quantification of human brain metabolites from magnetic resonance spectroscopy data Author(s) Shamaei, Amirmohammad (UPT-D)
Starčuková, Jana (UPT-D) RID, SAI, ORCID
Starčuk jr., Zenon (UPT-D) RID, ORCID, SAIArticle number 106837 Source Title Computers in Biology Medicine. - : Elsevier - ISSN 0010-4825
Roč. 158, May (2023)Number of pages 15 s. Publication form Print - P Language eng - English Country GB - United Kingdom Keywords MR spectroscopy ; Inverse problem ; Deep learning ; Machine learning ; Convolutional neural network ; Metabolite quantification Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects EF18_046/0016045 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) LM2018129 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) LM2023050 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) Method of publishing Open access Institutional support UPT-D - RVO:68081731 UT WOS 000982004200001 EID SCOPUS 85151756081 DOI 10.1016/j.compbiomed.2023.106837 Annotation 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. Workplace Institute of Scientific Instruments Contact Martina Šillerová, sillerova@ISIBrno.Cz, Tel.: 541 514 178 Year of Publishing 2024 Electronic address https://www.sciencedirect.com/science/article/pii/S0010482523003025
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