Search results
- 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/11104/0349228File Download Size Commentary Version Access 2024_Shamaei_MRM_EarlyAccess.pdf 2 3.9 MB Early access, OA - CC BY 4.0 https://creativecommons.org/licenses/by/4.0 Author’s postprint open-access - 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/11104/0342210File Download Size Commentary Version Access 2023_Shamaei_ComputersInBiologyMedicine.pdf 4 8.6 MB OA - CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Publisher’s postprint open-access
Research data: Zenodo - 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/11104/0338584File Download Size Commentary Version Access 2023_Rizzo_MRM.pdf 0 5.2 MB OA - CC BY-NC 4.0 https://creativecommons.org/licenses/by-nc/4.0/ Publisher’s postprint open-access Rizzo2023_ Quantification_MRM_EarlyAccess.pdf 3 5.2 MB Early access, OA CC BY-NC 4.0 Publisher’s postprint open-access - 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/11104/0335691File Download Size Commentary Version Access Shamaei2023_Model-informed_MRM.pdf 2 3.9 MB OA CC BY 4.0 Publisher’s postprint open-access Shamaei2022_MRM_Model-informed.pdf 12 4.9 MB Online First, OA CC BY 4.0 Publisher’s postprint open-access - 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/11104/0333741File Download Size Commentary Version Access Clarke2022_NIfTI‐MRS_MRM.pdf 3 4.7 MB CC BY 4.0 Publisher’s postprint open-access Clarke2022_NIfTI‐MRS.pdf 13 5.4 MB Online first, CC BY 4.0 Author’s postprint open-access
Research data: Zenodo