Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning
Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning
复制标题
DOI：
10.1016/j.media.2019.101589
复制
发表时间：
2020-01-01
影响因子：
10.9
通讯作者：
Suetens, Paul
中科院分区：
文献类型：
--
作者：
Robben, David;Boers, Anna M. M.;Suetens, Paul
CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke. Conventional perfusion analysis performs a deconvolution of the measurements and thresholds the perfusion parameters to determine the tissue status. We pursue a data-driven and deconvolution-free approach, where a deep neural network learns to predict the final infarct volume directly from the native CTP images and meta data such as the time parameters and treatment. This would allow clinicians to simulate various treatments and gain insight into predicted tissue status over time. We demonstrate on a multicenter dataset that our approach is able to predict the final infarct and effectively uses the metadata. An ablation study shows that using the native CTP measurements instead of the deconvolved measurements improves the prediction. (C) 2019 Published by Elsevier B.V.
