학술논문
Core and penumbra estimation using deep learning-based AIF in association with clinical measures in computed tomography perfusion (CTP)
Document Type
article
Author
Source
Insights into Imaging, Vol 14, Iss 1, Pp 1-10 (2023)
Subject
Language
English
ISSN
1869-4101
Abstract
Abstract Objectives To 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. Methods The 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. Results Penumbra estimated by CNN AIF correlated positively with the NIHSS score (r = 0.69; p 20) and lower ASPECT score ( 10 s, Tmax > 10 s volumes were statistically significantly higher (p