Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning

Abstract

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.

arXiv commentAccepted for publication in Medical Image Analysis

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Cited by in corpus (7)

• ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
• autoTICI: Automatic Brain Tissue Reperfusion Scoring on 2D DSA Images of Acute Ischemic Stroke Patients
• Combining unsupervised and supervised learning for predicting the final stroke lesion
• Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty
• 2.75D: Boosting learning by representing 3D Medical imaging to 2D features for small data
• CT Perfusion is All We Need: 4D CNN Segmentation of Penumbra and Core in Patients With Suspected Ischemic Stroke
• AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning