Search results

  1. 1.
    0580462 - ÚPT 2025 RIV US eng J - Journal Article
    Shamaei, Amirmohammad - Starčuková, Jana - Rizzo, R. - Starčuk jr., Zenon
    Water removal in MR spectroscopic imaging with Casorati singular value decomposition.
    Magnetic Resonance in Medicine. Roč. 91, č. 4 (2024), s. 1694-1706. ISSN 0740-3194. E-ISSN 1522-2594
    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 : functional MRS * low-rank approximations * MR spectroscopic imaging * water removal * water suppression
    Impact factor: 3.3, year: 2022
    Method of publishing: Open access
    https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.29959
    Permanent Link: https://hdl.handle.net/11104/0349228
    FileDownloadSizeCommentaryVersionAccess
    2024_Shamaei_MRM_EarlyAccess.pdf23.9 MBEarly access, OA - CC BY 4.0 https://creativecommons.org/licenses/by/4.0Author’s postprintopen-access
     
     
  2. 2.
    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
     
  3. 3.
    0567321 - ÚPT 2024 RIV US eng J - Journal Article
    Rizzo, R. - Dziadosz, M. - Kyathanahally, S. P. - Shamaei, Amirmohammad - Kreis, R.
    Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias.
    Magnetic Resonance in Medicine. Roč. 89, č. 5 (2023), s. 1707-1727. ISSN 0740-3194. E-ISSN 1522-2594
    EU Projects: European Commission(XE) 813120 - INSPiRE-MED
    Institutional support: RVO:68081731
    Keywords : active learning * bias * deep learning * ensemble of networks * model fitting * magnetic resonance spectroscopy * quantification
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    Impact factor: 3.3, year: 2022
    Method of publishing: Open access
    https://onlinelibrary.wiley.com/doi/10.1002/mrm.29561
    Permanent Link: https://hdl.handle.net/11104/0338584
    FileDownloadSizeCommentaryVersionAccess
    2023_Rizzo_MRM.pdf05.2 MBOA - CC BY-NC 4.0 https://creativecommons.org/licenses/by-nc/4.0/Publisher’s postprintopen-access
    Rizzo2023_ Quantification_MRM_EarlyAccess.pdf35.2 MBEarly access, OA CC BY-NC 4.0Publisher’s postprintopen-access
     
     
  4. 4.
    0563909 - ÚPT 2023 RIV US eng J - Journal Article
    Shamaei, Amirmohammad - Starčuková, Jana - Pavlova, Iveta - Starčuk jr., Zenon
    Model-informed unsupervised deep learning approaches to frequency and phase correction of MRS signals.
    Magnetic Resonance in Medicine. Roč. 89, č. 3 (2023), s. 1221-1236. ISSN 0740-3194. E-ISSN 1522-2594
    EU Projects: European Commission(XE) 813120 - INSPiRE-MED
    Institutional support: RVO:68081731
    Keywords : deep learning * edited MRS * frequency correction * MR spectroscopy * phase correction
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    Impact factor: 3.3, year: 2022
    Method of publishing: Open access
    https://onlinelibrary.wiley.com/doi/10.1002/mrm.29498
    Permanent Link: https://hdl.handle.net/11104/0335691
    FileDownloadSizeCommentaryVersionAccess
    Shamaei2023_Model-informed_MRM.pdf23.9 MBOA CC BY 4.0Publisher’s postprintopen-access
    Shamaei2022_MRM_Model-informed.pdf124.9 MBOnline First, OA CC BY 4.0Publisher’s postprintopen-access
     
     
  5. 5.
    0560982 - ÚPT 2023 RIV US eng J - Journal Article
    Clarke, W. T. - Bell, T. K. - Emir, U. E. - Mikkelsen, M. - Oeltzschner, G. - Shamaei, Amirmohammad - Soher, B. J. - Wilson, M.
    NIfTI-MRS: A standard data format for magnetic resonance spectroscopy.
    Magnetic Resonance in Medicine. Roč. 88, č. 6 (2022), s. 2358-2370. ISSN 0740-3194. E-ISSN 1522-2594
    EU Projects: European Commission(XE) 813120 - INSPiRE-MED
    Institutional support: RVO:68081731
    Keywords : MRS * MRSI * open data format * spectroscopy * visualization
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    Impact factor: 3.3, year: 2022
    Method of publishing: Open access
    https://onlinelibrary.wiley.com/doi/10.1002/mrm.29418
    Permanent Link: https://hdl.handle.net/11104/0333741
    FileDownloadSizeCommentaryVersionAccess
    Clarke2022_NIfTI‐MRS_MRM.pdf34.7 MBCC BY 4.0Publisher’s postprintopen-access
    Clarke2022_NIfTI‐MRS.pdf135.4 MBOnline first, CC BY 4.0Author’s postprintopen-access
     

    Research data: Zenodo
     


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