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

arXiv: https://arxiv.org/abs/1812.02496 · HTML: https://mlsanity.com/paper/1812.02496/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2018.

## Built on by (most cited first, 12 of 12)

- [ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset](https://mlsanity.com/paper/2206.06694/) (2022)
- [Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty](https://mlsanity.com/paper/2211.04161/) (2022)
- [ISLES'24 -- A Real-World Longitudinal Multimodal Stroke Dataset](https://mlsanity.com/paper/2408.11142/) (2024)
- [ISLES'24: Final Infarct Prediction with Multimodal Imaging and Clinical Data. Where Do We Stand?](https://mlsanity.com/paper/2408.10966/) (2024)
- [Combining unsupervised and supervised learning for predicting the final stroke lesion](https://mlsanity.com/paper/2101.00489/) (2021)
- [Automatic Prediction of Stroke Treatment Outcomes: Latest Advances and Perspectives](https://mlsanity.com/paper/2412.04812/) (2024)
- [Neural Network-derived perfusion maps: a Model-free approach to computed tomography perfusion in patients with acute ischemic stroke](https://mlsanity.com/paper/2101.05992/) (2021)
- [Analysing the effectiveness of a generative model for semi-supervised medical image segmentation](https://mlsanity.com/paper/2211.01886/) (2022)
- [CT Perfusion is All We Need: 4D CNN Segmentation of Penumbra and Core in Patients With Suspected Ischemic Stroke](https://mlsanity.com/paper/2303.08757/) (2023)
- [Final infarct prediction in acute ischemic stroke](https://mlsanity.com/paper/2211.04850/) (2022)
- [Ischemic Stroke Lesion Prediction using imbalanced Temporal Deep Gaussian Process (iTDGP)](https://mlsanity.com/paper/2211.09068/) (2022)
- [Automated Lesion Segmentation of Stroke MRI Using nnU-Net: A Comprehensive External Validation Across Acute and Chronic Lesions](https://mlsanity.com/paper/2601.08701/) (2026)

## Abstract (arXiv)

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.