Robust Quantification of Affected Brain Volume from Computed Tomography Perfusion: A Hybrid Approach Combining Deep Learning and Singular Value Decomposition.
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 accurate...
| Published in: | Journal of Imaging Informatics in Medicine Vol. 39; no. 3; pp. 2095 - 2110 |
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| Main Authors: | , , , , , , , , |
| Format: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Jun2026
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194225516&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194225516 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Jun2026 vid: 39 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 194225516 189894288 194225516 194225516 10.1007/s10278-025-01612-5 194225516 ppf: 2095 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Robust Quantification of Affected Brain Volume from Computed Tomography Perfusion: A Hybrid Approach Combining Deep Learning and Singular Value Decomposition. aug: au: Kim, Gi-Youn Yang, Hyeon Sik Hwang, Jundong Lee, Kijeong Choi, Jin Wook Jung, Woo Sang Kim, Regina Eun Young Kim, Donghyeon Lee, Minho affil: https://ror.org/00c33xm12 Research Institute, Neurophet Inc., 12F, 124, Teheran-ro, Seoul, Gangnam-gu, Republic of Korea sug: subj: Ischemic Stroke Diagnosis Brain Physiology Image Processing, Computer Assisted Tomography, X-Ray Computed Machine Learning Perfusion Imaging Deep Learning Prediction Models Human Algorithms Imaging, Three-Dimensional Random Forest Logistic Regression Descriptive Statistics Software Data Analysis Software Linear Regression Pearson's Correlation Coefficient Funding Source ab: 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), deep learning (DL), and machine learning (ML) techniques. We included 449 CTP images of patients with AIS with manually annotated vessel landmarks provided by expert radiologists, collected between 2021 and 2023. We developed a CNN-based approach for predicting eight vascular landmarks from CTP images, integrating ML components. We then used SVD-related methods to generate perfusion maps and compared the results with those of the RapidAI software (RapidAI, Menlo Park, California). The proposed CNN model achieved an average Euclidean distance error of 4.63 ± 2.00 mm on the vessel localization. Without the ML components, compared to RapidAI, our method yielded concordance correlation coefficient (CCC) scores of 0.898 for estimating volumes with cerebral blood flow (CBF) < 30% and 0.715 for Tmax > 6 s. Using the ML method, it achieved CCC scores of 0.905 for CBF < 30% and 0.879 for Tmax > 6 s. For the data assessment, it achieved 0.8 accuracy. We developed a robust hybrid model combining DL and ML techniques for volumetric estimation of affected brain volumes using CTP in patients with AIS, demonstrating improved accuracy and robustness compared to existing commercial solutions. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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