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Abstract #4714

Automatic Macro-Vessel-Minimization Using Independent Component Analysis and Local Scaling Functions

Gernot Reishofer1, Stephen Keeling2, Robert Merwa3, Christian Enzinger4, Stefan Ropele4, Rudolf Stollberger3, Franz Ebner5

1Department of Radiology / MR-Physics, Medical University of Graz, Graz, Austria; 2Institute for Mathematics and Scientific Computing, University of Graz, Graz, Austria; 3Institute of Medical Engineering, Graz University of Technology , Graz, Austria; 4Department of Neurology, Medical University of Graz, Graz, Austria; 5Department of Radiology / Division of Neuroradiology, Medical University of Graz, Graz, Austria

Independent component analysis has the potential to separate the signal assigned to macro vessels from tissue signal in DCE-MRI data. This enables an algorithm for minimizing macro-vessel signal in perfusion imaging to avoid overestimation of hemodynamic parameters. We set out to eliminate user dependency by automating the selection of appropriate ICs using a local scaling function technique. Furthermore we preserve the static tissue signal of the macro vessel representing IC by editing the time dependent mixing matrix in the ICA model before the back transformation is performed. The implemented algorithm is fast and stable and therefore applicable for clinical use.