Abstract #2866

Estimation of Tissue at Risk of Infarction Using a Support Vector Machine on Multimodal Stroke MRI Data

Susanne Schnell¹, Irina Mader², Dorothee Saur³, Kim Mouridsen⁴, Roza Umarova³, Dorothee Kmmerer³, Hans Burkhardt⁵, Valerij G. Kiselev¹

¹Dept. of Diagnostic Radiology, Medical Physics, University Medical Center Freiburg, Freiburg, Germany; ²Dept. of Neuroradiology, University Medical Center Freiburg, Freiburg, Germany; ³Dept. of Neurology, University Medical Center Freiburg, Freiburg, Germany; ⁴Dept. of Neuroradiology, Center for Functionally Integrative Neuroscience, Aarhus University Hospital, Aarhus, Denmark; ⁵Dept. of Computer Science, Chair in Pattern Recognition and Image Processing, Albert-Ludwigs University Freiburg, Freiburg, Germany

Acute ischemic stroke is a frequent cause of neurological disability. A clinical tool for the differentiation of reversable from irreversable damaged tissue would be of valuable use. This requires accurate prediction of the extent of the infarction. Typically, the estimation relies on models derived from perfusion and diffusion data (e.g. perfusion-diffusion mismatch, CBF, CBV). In this successfully performed trial study we introduce a data-driven method using Support Vector Machine (SVM) classification. The input data for the SVM consists of acute diffusion and perfusion information. A labelled training dataset is created using the patients follow-up scan in the chronic stage.