OBJECTIVE: Renal volumetry is an essential part of split renal function assessment in MR urography. The aim of this study was to assess the accuracy and repeatability of a 3D segmentation algorithm based on the belief functions theory for calculating renal volumes from MR images. MATERIALS AND METHODS: The true volumes of 20 animal kidneys of various sizes were obtained by fluid displacement. Each kidney was examined using two different MR units. Three-dimensional proton density-weighted acquisitions with an incremental slice thickness were performed. The MR volume was then measured with a segmentation algorithm based on the belief functions theory. Two independent observers performed all segmentations twice. Accuracy, intraobserver variability, and interobserver variability were evaluated by the Bland-Altman method. The number and type of manual corrections were recorded as well as the entire processing time. RESULTS: The mean renal volume estimated by fluid displacement was 114 mL (range, 38-224 mL). With regard to the renal volumes obtained from assessments of adjacent axial MR images, the maximal SDs of the difference were 2.2 mL (accuracy), 0.6 mL (intraobserver variability), and 1.8 mL (interobserver variability). Segmentation of axial slices provided better accuracy and reproducibility than coronal slices. Overlapped coronal slices yielded poor results because of the partial volume effect. The mean processing time including optional manual modifications was less than 75 seconds. CONCLUSION: The belief functions theory can be considered an accurate and reproducible mathematic method to assess renal volume from MR adjacent images.
OBJECTIVE: Renal volumetry is an essential part of split renal function assessment in MR urography. The aim of this study was to assess the accuracy and repeatability of a 3D segmentation algorithm based on the belief functions theory for calculating renal volumes from MR images. MATERIALS AND METHODS: The true volumes of 20 animal kidneys of various sizes were obtained by fluid displacement. Each kidney was examined using two different MR units. Three-dimensional proton density-weighted acquisitions with an incremental slice thickness were performed. The MR volume was then measured with a segmentation algorithm based on the belief functions theory. Two independent observers performed all segmentations twice. Accuracy, intraobserver variability, and interobserver variability were evaluated by the Bland-Altman method. The number and type of manual corrections were recorded as well as the entire processing time. RESULTS: The mean renal volume estimated by fluid displacement was 114 mL (range, 38-224 mL). With regard to the renal volumes obtained from assessments of adjacent axial MR images, the maximal SDs of the difference were 2.2 mL (accuracy), 0.6 mL (intraobserver variability), and 1.8 mL (interobserver variability). Segmentation of axial slices provided better accuracy and reproducibility than coronal slices. Overlapped coronal slices yielded poor results because of the partial volume effect. The mean processing time including optional manual modifications was less than 75 seconds. CONCLUSION: The belief functions theory can be considered an accurate and reproducible mathematic method to assess renal volume from MR adjacent images.
Authors: Pierre-Hugues Vivier; Pippa Storey; Henry Rusinek; Jeff L Zhang; Akira Yamamoto; Kristopher Tantillo; Umer Khan; Ruth P Lim; James S Babb; Devon John; Lewis W Teperman; Hersh Chandarana; Kent Friedman; Judith A Benstein; Edward Y Skolnik; Vivian S Lee Journal: Radiology Date: 2011-03-08 Impact factor: 11.105