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.