A Variable-Kernel Smoothing Technique for Improved Convergence During CT-Cone Beam CT Deformable Image Registration
V Hart1*, X Li1, (1) Medical College of Wisconsin, Milwaukee, WITU-C-141-5 Tuesday 10:30AM - 12:30PM Room: 141
Purpose: To develop a fast and accurate technique for deformable image registration (DIR) of cone beam CT (CBCT) and CT images for use in online adaptive radiotherapy (ART).
Methods: A novel technique for smoothing a deformation field in which the size of the Gaussian kernel is adaptively varied based on the rate of convergence is proposed and implemented for DIR based on the demons algorithm. The planning CT and daily CBCT acquired for 6 prostate cancer patients were registered using the newly developed technique. Histogram matching was used to compensate for intensity differences between the two modalities. The Pearson correlation coefficient (PCC) and volume overlap index (VOI) were used to quantify registration accuracy.
Results: The iterative decrease of the smoothing kernel size allowed the algorithm to converge to an increasingly accurate solution, beyond the asymptotic limit for a constant kernel size. The mean VOI was calculated for bladder, prostate, and rectum with values of 91.9%, 68.7%, and 78.2%, respectively. The correlation coefficient was calculated for every fraction in the overlapping CT and CBCT scan volumes in each patient data set. Average PCC values were 0.9987, 0.9985, 0.9982, 0.9980, 0.9985, and 0.9985 for the six patients. A typical run time for a 512x512x70 image volume was 4.6 minutes.
Conclusion: The presently developed DIR technique allowed for registration accuracies comparable to intra-modality methods and validates its use for deformable CT-CBCT registration. The method eliminates the need for multi-resolution processing and continual upsampling of the image which can be computationally intensive. The combined speed and accuracy of this technique make it a promising tool for online ART.