Core and penumbra estimation using deep learning-based AIF in association with clinical measures in computed tomography perfusion (CTP)

被引：0

作者：

Sukhdeep Singh Bal

Fan-pei Gloria Yang

Nai-Fang Chi

Jiu Haw Yin

Tao-Jung Wang

Giia Sheun Peng

Ke Chen

Ching-Chi Hsu

Chang-I Chen

机构：

[1] University of Liverpool,Department of Mathematical Sciences

[2] National Tsing Hua University,Center for Cognition and Mind Sciences

[3] National Tsing Hua University,Department of Foreign Languages and Literature

[4] Osaka University,Department of Radiology, Graduate School of Dentistry

[5] Taipei Veterans General Hospital,Neurological Institute

[6] National Defense Medical Center,Department of Neurology, Tri

[7] National Yang Ming Chiao Tung University,Service General Hospital

[8] Taipei Veterans General Hospital,Department of Computer Science

[9] Wizcare Medical Corporation Aggregate,Division of Neurology, Department of Internal Medicine

[10] Taipei Medical University,Board of Directors

来源：

Insights into Imaging | / 14卷

关键词：

Arterial input function; Ischemic stroke; Core; Penumbra; Perfusion parameters;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

CNN-based AIF improves the estimation of penumbra and infarct core volumes.CNN AIF identifies patients with core regions which were ignored by conventional approaches.Core and penumbra assessment with CNN AIF correlates well with clinical scores.

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