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Abstract #0552

Combined unsupervised-supervised classification of multiparametric PET/MRI imaging data of the prostate

Sergios Gatidis 1 , Petros Martirosian 1 , Thomas Kstner 1 , Ilja Bezrukov 2 , Marcus Scharpf 3 , Christina Schraml 1 , Nina F Schwenzer 1 , and Holger Schimdt 1,2

1 Department of Radiology, University of Tbingen, Tbingen, BW, Germany, 2 Department of Preclinical Imaging and Radiopharmacy, University of Tbingen, Tbingen, BW, Germany, 3 Department of Pathology, University of Tbingen, Tbingen, BW, Germany

In this study we implemented and evaluated a combined unsupervised-supervised classification algorithm for the analysis of multiparametrc PET/MRI data that allows for robust classification in cases where prior knowledge about the data is limited. We applied the proposed method to [11C]-Choline-PET/MRI data of the prostate and observed high classification accuracy compared to manual tumor delineation and to histological slices. Numerous applications of this approach are conceivable, especially in areas where histopathological correlation is difficult (e.g. brain imaging) and thus knowledge about ground truth is limited.

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