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.