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 metadata 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.

Additional Metadata
Keywords CT Perfusion, Deep learning, Final infarct prediction, Stroke
Persistent URL dx.doi.org/10.1016/j.media.2019.101589, hdl.handle.net/1765/120878
Journal Medical Image Analysis
Citation
Robben, D. (David), Boers, A.M.M, Marquering, H, Langezaal, L.L.C.M. (Lucianne L.C.M.), Roos, Y.B.W.E.M, van Oostenbrugge, R.J, … Suetens, P. (Paul). (2020). Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning. Medical Image Analysis, 59. doi:10.1016/j.media.2019.101589