Wavelet-based segmentation of renal compartments in DCE-MRI of human kidney

• Verfasst von: Li, Sheng [VerfasserIn]
• Verfasst von: Zöllner, Frank G. [VerfasserIn]
• Verfasst von: Merrem, Andreas D. [VerfasserIn]
• Verfasst von: Schad, Lothar R. [VerfasserIn]
• Titel: Wavelet-based segmentation of renal compartments in DCE-MRI of human kidney
• Titelzusatz: initial results in patients and healthy volunteers
• Verf.angabe: Sheng Li, Frank G.Zöllner, Andreas D.Merrem, Yinghong Peng, Jarle Roervik, Arvid Lundervold, Lothar R.Schad
• E-Jahr: 2012
• Jahr: March 2012
• Umfang: 11 S.
• Fussnoten: Available online 24 June 2011 ; Gesehen am 06.11.2018
• Titel Quelle: Enthalten in: Computerized medical imaging and graphics
• Ort Quelle: Amsterdam [u.a.] : Elsevier Science, 1988
• Jahr Quelle: 2012
• Band/Heft Quelle: 36(2012), 2, Seite 108-118
• ISSN Quelle: 1879-0771
• DOI: doi:10.1016/j.compmedimag.2011.06.005
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 158263307X

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.