title: Wavelet-based segmentation of renal compartments in DCE-MRI of human kidney: initial results in patients and healthy volunteers.
abstract: Renal diseases can lead to kidney failure that requires life-long dialysis or renal transplantation. Early detection and treatment can prevent progression towards end stage renal disease. MRI has evolved into a standard examination for the assessment of the renal morphology and function. We propose a wavelet-based clustering to group the voxel time courses and thereby, to segment the renal compartments. This approach comprises (1) a nonparametric, discrete wavelet transform of the voxel time course, (2) thresholding of the wavelet coefficients using Stein's Unbiased Risk estimator, and (3) k-means clustering of the wavelet coefficients to segment the kidneys. Our method was applied to 3D dynamic contrast enhanced (DCE-) MRI data sets of human kidney in four healthy volunteers and three patients. On average, the renal cortex in the healthy volunteers could be segmented at 88%, the medulla at 91%, and the pelvis at 98% accuracy. In the patient data, with aberrant voxel time courses, the segmentation was also feasible with good results for the kidney compartments. In conclusion wavelet based clustering of DCE-MRI of kidney is feasible and a valuable tool towards automated perfusion and glomerular filtration rate quantification.
authors.affiliation: Computer Assisted Clinical Medicine, Heidelberg University, Germany.
authors.forename: Sheng
authors.initials: S
authors.lastname: Li
authors.forename: Frank G
authors.initials: FG
authors.lastname: Zöllner
authors.forename: Andreas D
authors.initials: AD
authors.lastname: Merrem
authors.forename: Yinghong
authors.initials: Y
authors.lastname: Peng
authors.forename: Jarle
authors.initials: J
authors.lastname: Roervik
authors.forename: Arvid
authors.initials: A
authors.lastname: Lundervold
authors.forename: Lothar R
authors.initials: LR
authors.lastname: Schad
mesh_terms: D000465:Algorithms; D003287:Contrast Media; D005260:Female; D005682:Gadolinium; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007668:Kidney / Q000473:pathology*; D007674:Kidney Diseases / Q000473:pathology*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D058067:Wavelet Analysis
chemical_list: D003287:Contrast Media; D005682:Gadolinium