Computational Boundary Sampling to Accelerate IMRT Optimization
P Tiwari1, Y Xie1, Y Chen1, A Apte2, J Deasy2*, (1) Washington University in St. Louis, Saint Louis, Missouri, (2) Memorial Sloan Kettering Cancer Center, NEW YORK, NYSU-E-T-620 Sunday 3:00:00 PM - 6:00:00 PM Room: Exhibit Hall
Purpose: To reduce the time and memory requirements of Intensity Modulated Radiation Therapy (IMRT) treatment planning.
Methods: 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.
Results: (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 compared our original IMRT optimization solver without sampling and the solver combined with CBS sampling. The result showed that CBS can reduce the solution time and memory consumption by up to 20x with < 2% change in dosimetric variables.
Conclusions: We have proposed a new sampling method (CBS), along with corresponding new techniques including boundary sampling and grid sampling, to improve time and space efficiency of IMRT optimization. A corresponding theory is developed to quantify the error bound. Experimental results have shown that our new methods significantly reduce solution time and memory costs with negligible impact on resulting plan quality.