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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 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Physics-informed deep learning approach to quantification of human brain metabolites from magnetic resonance spectroscopy data Tvůrce(i) Shamaei, Amirmohammad (UPT-D)
Starčuková, Jana (UPT-D) RID, SAI, ORCID
Starčuk jr., Zenon (UPT-D) RID, ORCID, SAIČíslo článku 106837 Zdroj.dok. Computers in Biology Medicine. - : Elsevier - ISSN 0010-4825
Roč. 158, May (2023)Poč.str. 15 s. Forma vydání Tištěná - P Jazyk dok. eng - angličtina Země vyd. GB - Velká Británie Klíč. slova MR spectroscopy ; Inverse problem ; Deep learning ; Machine learning ; Convolutional neural network ; Metabolite quantification Vědní obor RIV IN - Informatika Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) CEP EF18_046/0016045 GA MŠMT - Ministerstvo školství, mládeže a tělovýchovy LM2018129 GA MŠMT - Ministerstvo školství, mládeže a tělovýchovy LM2023050 GA MŠMT - Ministerstvo školství, mládeže a tělovýchovy Způsob publikování Open access Institucionální podpora UPT-D - RVO:68081731 UT WOS 000982004200001 EID SCOPUS 85151756081 DOI 10.1016/j.compbiomed.2023.106837 Anotace 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. Pracoviště Ústav přístrojové techniky Kontakt Martina Šillerová, sillerova@ISIBrno.Cz, Tel.: 541 514 178 Rok sběru 2024 Elektronická adresa https://www.sciencedirect.com/science/article/pii/S0010482523003025
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