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    0570880 - ÚPT 2024 RIV GB eng J - Journal Article
    Shamaei, Amirmohammad - Starčuková, Jana - Starčuk jr., Zenon
    Physics-informed deep learning approach to quantification of human brain metabolites from magnetic resonance spectroscopy data.
    Computers in Biology Medicine. Roč. 158, May (2023), č. článku 106837. ISSN 0010-4825. E-ISSN 1879-0534
    R&D Projects: GA MŠMT(CZ) EF18_046/0016045; GA MŠMT(CZ) LM2018129; GA MŠMT(CZ) LM2023050
    EU Projects: European Commission(XE) 813120 - INSPiRE-MED
    Institutional support: RVO:68081731
    Keywords : MR spectroscopy * Inverse problem * Deep learning * Machine learning * Convolutional neural network * Metabolite quantification
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    Impact factor: 7.7, year: 2022
    Method of publishing: Open access
    https://www.sciencedirect.com/science/article/pii/S0010482523003025
    Permanent Link: https://hdl.handle.net/11104/0342210
    FileDownloadSizeCommentaryVersionAccess
    2023_Shamaei_ComputersInBiologyMedicine.pdf48.6 MBOA - CC BY 4.0 https://creativecommons.org/licenses/by/4.0/Publisher’s postprintopen-access
     

    Research data: Zenodo
     

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