ArticleFrontiers in neuroscience2022
Predicting the tissue outcome of acute ischemic stroke from acute 4D computed tomography perfusion imaging using temporal features and deep learning.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Differentiating effective salvage from ineffective delayed compensation in anterior circulation stroke: a dynamic quantitative collateral index.Frontiers in medicine · 2026Article
- A multimodal multitask deep learning model for predicting stroke lesion and functional outcomes using 4D CTP imaging and clinical metadata.Scientific reports · 2025Article
- Novel Computed Tomography Perfusion and Laboratory Indices as Predictors of Long-Term Outcome and Survival in Acute Ischemic Stroke.Neurology international · 2025Article
- Automatic prediction of stroke treatment outcomes: latest advances and perspectives.Biomedical engineering letters · 2025Review
- Challenges and Potential of Artificial Intelligence in Neuroradiology.Clinical neuroradiology · 2024Review
- A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke.Frontiers in neurology · 2024Review
- Prediction of tissue outcome in acute ischemic stroke based on single-phase CT angiography at admission.Frontiers in neurology · 2024Article
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Authors and funding
6 authors.
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Abstract
Predicting follow-up lesions from baseline CT perfusion (CTP) datasets in acute ischemic stroke patients is important for clinical decision making. Deep convolutional networks (DCNs) are assumed to be the current state-of-the-art for this task. However, many DCN classifiers have not been validated against the methods currently used in research (random decision forests, RDF) and clinical routine (Tmax thresholding). Specialized DCNs have even been designed to extract complex temporal features directly from spatiotemporal CTP data instead of using standard perfusion parameter maps. However, the benefits of applying deep learning to source or deconvolved CTP data compared to perfusion parameter maps have not been formally investigated so far. In this work, a modular UNet-based DCN is proposed that separates temporal feature extraction from tissue outcome prediction, allowing for both model validation using perfusion parameter maps as well as end-to-end learning from spatiotemporal CTP data. 145 retrospective datasets comprising baseline CTP imaging, perfusion parameter maps, and follow-up non-contrast CT with manual lesion segmentations were assembled from acute ischemic stroke patients treated with intravenous thrombolysis alone (IV;
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