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Abstract #2586

Classification of Parkinson’s disease based on multi-parametric data derived from MR Fingerprinting measurements

Thomas Amthor1, Peter Koken1, Mariya Doneva1, Vera Catharina Keil2, Stilyana Peteva Bakoeva2, Alina Jurcoane2,3, Wolfgang Block2, Ullrich Wüllner4,5, Burkhard Mädler6, and Elke Hattingen2,3

1Philips Research Europe, Hamburg, Germany, 2Department of Radiology, University Hospital Bonn, Bonn, Germany, 3Institute for Neuroradiology, University Hospital Frankfurt/Main, Frankfurt, Germany, 4Department of Neurology, University Hospital Bonn, Bonn, Germany, 5German Centre for neurodegenerative disease research (DZNE), Bonn, Germany, 6Philips Healthcare, Bonn, Germany

We investigated the potential of multi-parametric MR Fingerprinting measurements for the classification of Parkinson’s disease. For each measured quantity (T1, T2, proton density) and each segmented brain region, several statistical parameters were determined and used to train a Random Forest classification algorithm. An AUC of 0.92 was achieved for distinguishing Parkinson patients from healthy control subjects.

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