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

Gi-Youn Kim¹, Hyeon Sik Yang¹, Jundong Hwang¹, Kijeong Lee¹, Jin Wook Choi², Woo Sang Jung², Regina Eun Young Kim¹, Donghyeon Kim¹, Minho Lee³

¹Research Institute, Neurophet Inc., 12F, 124, Teheran-ro, Gangnam-gu, Seoul, Republic of Korea.

²Department of Radiology, Ajou University School of Medicine, Suwon, Republic of Korea.

³Research Institute, Neurophet Inc., 12F, 124, Teheran-ro, Gangnam-gu, Seoul, Republic of Korea. minho.lee@neurophet.com.

PubMed PMC DOI PDF

Abstract

Volumetric estimation of affected brain volumes using computed tomography perfusion (CTP) is crucial in the management of acute ischemic stroke (AIS) and relies on commercial software, which has limitations such as variations in results due to image quality. To predict affected brain volume accurately and robustly, we propose a hybrid approach that integrates singular value decomposition (SVD), de…

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