The ISLES’24 Dataset: A Multimodal Stroke Imaging Dataset with Hyperacute CT, Acute Postinterventional MRI, and 3-month Clinical Outcomes

Abstract

Stroke remains a major global health burden (1,2), although outcomes have improved substantially through imaging-guided therapy and endovascular reperfusion (3,4). While CT and MRI are standard for estimating infarct core and penumbra (5), variability in threshold-based deconvolution of perfusion imaging (6) can lead to inconsistent lesion size estimates (7). Accurate modeling of infarct growth is therefore essential for optimizing transfer and treatment decisions (8). Advances in artificial intelligence (AI) have improved automated lesion detection, yet clinical translation requires large, well-annotated datasets. While recent large-scale cohorts including the Ischemic Stroke Lesion Segmentation Challenge (ISLES)’22 ( n = 400) (9), Liew et al ( n = 1271) (10), Liu et al ( n = 2888) (11), and Absher et al ( n = 1715) datasets (12) have expanded available imaging data, datasets pairing acute CT with follow-up MRI (13) remain limited. We address this gap by providing a publicly available dataset that combines hyperacute CT (< 24 h post onset) with acute postinterventional MRI (2–9 days after successful reperfusion; modified Treatment in Cerebral Ischemia 2c or 3) and structured clinical follow-up through 3 months. This combination enables analysis of infarct evolution and supports AI model development for postinterventional stroke care. © RSNA, 2026

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