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Robust Quantification of Affected Brain Volume from Computed Tomography Perfusion: A Hybrid Approach Combining Deep Learning and Singular Value Decomposition.

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Bibliographic Details
Title:	Robust Quantification of Affected Brain Volume from Computed Tomography Perfusion: A Hybrid Approach Combining Deep Learning and Singular Value Decomposition.
Authors:	Kim GY; Research Institute, Neurophet Inc., 12F, 124, Teheran-ro, Gangnam-gu, Seoul, Republic of Korea., Yang HS; Research Institute, Neurophet Inc., 12F, 124, Teheran-ro, Gangnam-gu, Seoul, Republic of Korea., Hwang J; Research Institute, Neurophet Inc., 12F, 124, Teheran-ro, Gangnam-gu, Seoul, Republic of Korea., Lee K; Research Institute, Neurophet Inc., 12F, 124, Teheran-ro, Gangnam-gu, Seoul, Republic of Korea., Choi JW; Department of Radiology, Ajou University School of Medicine, Suwon, Republic of Korea., Jung WS; Department of Radiology, Ajou University School of Medicine, Suwon, Republic of Korea., Kim REY; Research Institute, Neurophet Inc., 12F, 124, Teheran-ro, Gangnam-gu, Seoul, Republic of Korea., Kim D; Research Institute, Neurophet Inc., 12F, 124, Teheran-ro, Gangnam-gu, Seoul, Republic of Korea., Lee M; Research Institute, Neurophet Inc., 12F, 124, Teheran-ro, Gangnam-gu, Seoul, Republic of Korea. minho.lee@neurophet.com.
Source:	Journal of imaging informatics in medicine [J Imaging Inform Med] 2026 Jun; Vol. 39 (3), pp. 2095-2109. Date of Electronic Publication: 2025 Aug 27.
Publication Type:	Journal Article
Journal Info:	Publisher: Springer Nature Country of Publication: Switzerland NLM ID: 9918663679206676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2948-2933 (Electronic) Linking ISSN: 29482925 NLM ISO Abbreviation: J Imaging Inform Med Subsets: MEDLINE
Database:	MEDLINE Ultimate

Description
ISSN:	2948-2933
DOI:	10.1007/s10278-025-01612-5