Physics-informed deep learning approach to quantification of human brain metabolites from magnetic resonance spectroscopy data

Number of the records: 1

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SYSNO	0570880
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, SAI}
Source Title	Computers in Biology Medicine. Roč. 158, May (2023). - : Elsevier
Article number	106837
Document Type	Článek v odborném periodiku
Grant	813120, XE - EU countries
	EF18_046/0016045 GA MŠMT - Ministry of Education, Youth and Sports (MEYS), CZ - Czech Republic
	LM2018129 GA MŠMT - Ministry of Education, Youth and Sports (MEYS), CZ - Czech Republic
	LM2023050 GA MŠMT - Ministry of Education, Youth and Sports (MEYS), CZ - Czech Republic
Institutional support	UPT-D - RVO:68081731
Language	eng
Country	GB
Keywords	MR spectroscopy * Inverse problem * Deep learning * Machine learning * Convolutional neural network * Metabolite quantification
Other sources	https://zenodo.org/record/3828935
Cooperating institutions	Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií (Czech Republic)
URL	https://www.sciencedirect.com/science/article/pii/S0010482523003025
Permanent Link	https://hdl.handle.net/11104/0342210

	File	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

Number of the records: 1