ISLES'24 - A Real-World Longitudinal Multimodal Stroke Dataset

This multi-center dataset consists of 149 acute ischemic stroke cases, representing the training set of the ISLES'24 challenge. All data are provided in NIfTI format (.nii.gz) and organized according to the BIDS standard. For each case, the following data are included: Admission imaging: non-contrast CT (NCCT), CT angiography (CTA), 4D CT perfusion (CTP) time series, and perfusion maps (Tmax, CBF, CBV, MTT). Follow-up imaging: post-treatment MRI (DWI and ADC). Clinical data: demographics, patient history, admission NIHSS, 3‑month functional outcome (mRS), etc. Annotations: binary infarct masks derived from follow-up MRI (lesion-msk.nii.gz), large vessel occlusion binary masks derived from CTA (lvo-msk.nii.gz), and the multi-labeled Circle of Willis anatomy generated with an automatic algorithm over CTA (cow-msk.nii.gz) This dataset combines multimodal imaging, longitudinal follow-up, and structured clinical variables to support benchmarking of stroke infarct prediction methods. Data structure 'Raw_data' refers to the 'raw' acquired scans, which are released in their original space, just defaced. 'Derivatives' include all modalities linearly co-registered to the NCCT space. Ses-0001 points to the acute imaging data, while Ses-0002 refers to the follow-up imaging data (sub-acute stroke phase). A single case-sample is structured as follows. raw_data/├── sub-strokecase0001/│ └── ses-0001/│ ├── perfusion-maps/│ │ ├── sub-strokecase0001_ses-0001_tmax.nii.gz│ │ ├── sub-strokecase0001_ses-0001_mtt.nii.gz│ │ ├── sub-strokecase0001_ses-0001_cbf.nii.gz│ │ └── sub-strokecase0001_ses-0001_cbv.nii.gz│ ├── sub-strokecase0001_ses-0001_ncct.nii.gz│ ├── sub-strokecase0001_ses-0001_cta.nii.gz│ └── sub-strokecase0001_ses-0001_ctp.nii.gz derivatives/├── sub-strokecase0001/│ ├── ses-0001/│ │ ├── perfusion-maps/│ │ │ ├── sub-strokecase0001_ses-0001_space-ncct_tmax.nii.gz│ │ │ ├── sub-strokecase0001_ses-0001_space-ncct_mtt.nii.gz│ │ │ ├── sub-strokecase0001_ses-0001_space-ncct_cbf.nii.gz│ │ │ └── sub-strokecase0001_ses-0001_space-ncct_cbv.nii.gz│ │ ├── sub-strokecase0001_ses-0001_space-ncct_cta.nii.gz│ │ ├── sub-strokecase0001_ses-0001_space-ncct_ctp.nii.gz│ │ ├── sub-stroke0086_ses-01_space-ncct_cow-msk.nii.gz│ │ └── sub-stroke0086_ses-01_space-ncct_lvo-msk.nii.gz│ └── ses-0002/│ ├── sub-strokecase0001_ses-02_space-ncct_dwi.nii.gz│ ├── sub-strokecase0001_ses-02_space-ncct_adc.nii.gz│ └── sub-strokecase0001_ses-02_space-ncct_lesion-msk.nii.gz phenotype/├── ses-0001/│ └── sub-strokecase0001_ses-0001_demographic_baseline.csv└── ses-0002/ └── sub-strokecase0001_ses-0001_outcome.csv Please cite the following two works when using this dataset: Riedel, O. E., de la Rosa, E., Hernandez Petzsche, M., Baazaoui, H., Yang, K., Musio, F. A., … & Kirschke, J. S. (2024). ISLES’24 – A Real-World Longitudinal Multimodal Stroke Dataset. arXiv e-prints, arXiv:2408.09259. de la Rosa, E., Su, R., Reyes, M., Wiest, R., Riedel, E. O., Kofler, F., … & Menze, B. (2024). ISLES’24: Final Infarct Prediction with Multimodal Imaging and Clinical Data. Where Do We Stand? arXiv preprint, arXiv:2408.10966. If you use the Circle of Willis masks, please ALSO cite: Yang, K., Musio, F., Ma, Y., Juchler, N., Paetzold, J. C., Al-Maskari, R., ... & Menze, B. (2024). Benchmarking the cow with the topcow challenge: Topology-aware anatomical segmentation of the circle of willis for cta and mra. ArXiv, arXiv-2312.