SU-E-T-620: Computational Boundary Sampling to Accelerate IMRT Optimization

Medical Physics
P TiwariJ O Deasy

Abstract

To reduce the time and memory requirements of Intensity Modulated Radiation Therapy (IMRT) treatment planning. We propose a new sampling method, called Computational Boundary Sampling (CBS) for IMRT optimization, which samples all the boundary voxels and a certain percentage of inner voxels of each region of interest (ROI). Within CBS, we developed a grid-based sampling method for choosing inner voxels. In this method, each region is first evenly gridded and then sampling points are randomly selected from each sub-volume. We also developed a supporting theory to quantify the solution quality of CBS. We compared a variant of CBS that always keeps boundary voxels and a variant of CBS that does not. Finally, we quantified the impact of CBS on 10 different anonymized, clinical treatment cases using a prioritized prescription optimization method, including compute time, required memory and objective function values. (1) We have found that the D95 of the targets are generally 4% larger when boundary voxels are included. (2) Grid sampling, compared to completely random sampling, yields more uniformly distributed sampling, with better solution quality, and less variance between independent runs, using the same or less time. (3) We have...Continue Reading

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