We developed an efficient technique to auto-propagate parotid gland contours from planning kVCT to daily MVCT images of head-and-neck cancer patients treated with helical tomotherapy. The method deformed a 3D surface mesh constructed from manual kVCT contours by B-spline free-form deformation to generate optimal and smooth contours. Deformation was calculated by elastic image registration between kVCT and MVCT images. Data from ten head-and-neck cancer patients were considered and manual contours by three observers were included in both kVCT and MVCT images. A preliminary inter-observer variability analysis demonstrated the importance of contour propagation in tomotherapy application: a high variability was reported in MVCT parotid volume estimation (p = 0.0176, ANOVA test) and a larger uncertainty of MVCT contouring compared with kVCT was demonstrated by DICE and volume variability indices (Wilcoxon signed rank test, p <10-4 for both indices). The performance analysis of our method showed no significant differences between automatic and manual contours in terms of volumes (p > 0.05, in a multiple comparison Tukey test), center-of-mass distances (p = 0.3043, ANOVA test), DICE values (p = 0.1672, Wilcoxon signed rank test) and average and maximum symmetric distances (p = 0.2043, p = 0.8228 Wilcoxon signed rank tests). Results suggested that our contour propagation method could successfully substitute human contouring on MVCT images.
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
- Radiological and Ultrasound Technology