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.