Abstract #1412

CNN-based classification of multiple sclerosis using BOLD venographic imaging (SWI) data

Alina Lopatina¹, Renat Sibgatulin¹, Stefan Ropele², Jürgen R Reichenbach^1,3, and Daniel Güllmar¹

¹Medical Physics Group / IDIR, Jena University Hospital, Jena, Germany, ²Department of Neurology, Medical University of Graz, Graz, Austria, ³Michael Stifel Center for Data-driven Sciences, Friedrich-Schiller-University Jena, Jena, Germany

Convolutional neural network (CNN) was proposed to identify multiple sclerosis patients and healthy subjects while susceptibility-weighted imaging (SWI) was used for MRI scans preprocessing in order to disclose important features hidden in brain venograms. Using only one two-dimensional slice for each subject makes the proposed algorithm easy-applied and useful for clinical practice.

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