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Abstract #1318

Renal Compartment Segmentation by Wavelet-Based Clustering of 3D DCE-MRI of Human Kidney

Frank G. Zoellner1, 2, Sheng Li1, 3, Andreas D. Merrem1, Jarle Roervik, 24, Arvid Lundervold, 45, Lothar R. Schad1

1Computer Assisted Clinical Medicine, Heidelberg University, Mannheim, Germany; 2Section for Radiology, Dept. of Surgical Sciences, University of Bergen, Bergen, Norway; 3Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; 4Dept. of Radiology, Haukeland University Hospital, Bergen, Norway; 5Dept. of Biomedicine, University of Bergen, Bergen, Norway

Correct determination (segmentation) of the renal compartments within the images is crucial to obtain i.e. whole kidney GFR via pharmacokinetic modelling. We propose a wavelet-based segmentation method to group the voxel time courses and thereby segment the renal compartments. This method was applied to DCE-MRI data sets of 4 healthy volunteers and 3 patients. On average, the renal cortex could be segmented at 88%, the medulla at 91%, and the pelvis at 98% accuracy. Time intensity curves showed well known characteristics of perfusion time curves for the respective compartments. In conclusion, wavelet based clustering of DCE-MRI of kidney is feasible.