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

Insights into Imaging

Original Article

Ching-Chi Hsu¹  Fan-pei Gloria Yang²  Tao-Jung Wang³  Ke Chen⁴  Sukhdeep Singh Bal⁵  Chang-I Chen⁶  Jiu Haw Yin⁷  Giia Sheun Peng⁸  Nai-Fang Chi⁹ 

[1] Board of Directors, Wizcare Medical Corporation Aggregate, Taichung, Taiwan;Center for Cognition and Mind Sciences, National Tsing Hua University, Hsinchu, Taiwan;Department of Foreign Languages and Literature, National Tsing Hua University, Hsinchu, Taiwan;Department of Radiology, Graduate School of Dentistry, Osaka University, Suita, Japan;Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan;Department of Mathematical Sciences, University of Liverpool, Liverpool, Merseyside, UK;Department of Mathematical Sciences, University of Liverpool, Liverpool, Merseyside, UK;Center for Cognition and Mind Sciences, National Tsing Hua University, Hsinchu, Taiwan;Department of Medical Management, Taipei Medical University, Taipei, Taiwan;Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan;Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan;Division of Neurology, Department of Internal Medicine, Taipei Veterans General Hospital, Hsinchu Branch, Hsinchu County, Taipei, Taiwan;Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan;

关键词: Arterial input function;    Ischemic stroke;    Core;    Penumbra;    Perfusion parameters;   

DOI  :  10.1186/s13244-023-01472-z

 received in 2023-04-18, accepted in 2023-06-23,  发布年份 2023

来源: Springer

【 摘 要 】

ObjectivesTo investigate whether utilizing a convolutional neural network (CNN)-based arterial input function (AIF) improves the volumetric estimation of core and penumbra in association with clinical measures in stroke patients.MethodsThe study included 160 acute ischemic stroke patients (male = 87, female = 73, median age = 73 years) with approval from the institutional review board.The patients had undergone CTP imaging, NIHSS and ASPECTS grading. convolutional neural network (CNN) model was trained to fit a raw AIF curve to a gamma variate function. CNN AIF was utilized to estimate the core and penumbra volumes which were further validated with clinical scores.ResultsPenumbra estimated by CNN AIF correlated positively with the NIHSS score (r = 0.69; p < 0.001) and negatively with the ASPECTS (r =  − 0.43; p < 0.001). The CNN AIF estimated penumbra and core volume matching the patient symptoms, typically in patients with higher NIHSS (> 20) and lower ASPECT score (< 5). In group analysis, the median CBF < 20%, CBF < 30%, rCBF < 38%, Tmax > 10 s, Tmax > 10 s volumes were statistically significantly higher (p < .05).ConclusionsWith inclusion of the CNN AIF in perfusion imaging pipeline, penumbra and core estimations are more reliable as they correlate with scores representing neurological deficits in stroke.Critical relevance statementWith CNN AIF perfusion imaging pipeline, penumbra and core estimations are more reliable as they correlate with scores representing neurological deficits in stroke.Graphic abstract

【 授权许可】

CC BY   
© European Society of Radiology (ESR) 2023

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