TH‐C‐137‐06: An Inter‐Fractional Morphing Aperture Based Reoptimization Tool for Adaptive Radiotherapy

Abstract

Purpose: To dosimetrically evaluate a streamlined adaptive radiotherapy tool to account for inter‐fractional tumor shrinkage. A novel aperture‐morphed simultaneously optimized finite sized pencil beam algorithm (AMSO‐FSPB) was investigated over volumetric modulated arc therapy (VMAT) head and neck plans. Methods: AMSO‐FSPB plans were retrospectively designed for head and neck patients in the supine position. CT image sets between fractions were rigidly registered using normalized cross correlation. A demons based deformable image registration algorithm was used to obtain a deformation vector field (DVF) between fractions. The DVFs were used to map contours between fractions. An eclipse and pinnacle compatible contour mapping algorithm was developed to apply the DVFs to the structure dicom set. DRRs at each gantry angle were used to derive the ratio of tumor shrinkage at every MLC position. The DVF and the target contour shrinkage were used to obtain a new set of fluence maps. The machine tolerance guidelines for the Varian True beam were followed for this study. A graphics processor united (GPU) based FSPB dose calculation engine with an embedded weighting factor re‐optimization algorithm was developed to reassign beamlet weighting factors to every 4 degrees of arch length in the VMAT plan. Results: The dosimetric advantages of the AMSO‐FSPB VMAT plans were directly proportional to the amount of the tumor regression between fractions. AMSO‐FSPB VMAT plans were dosimetrically superior to the original plans for all cases. The ratio of the mean and maximum dose for the Paratoids, Spinal Cord, and Brain Stem were lowered for all cases. The mean for the PTV70Gy, PTV60Gy, and PTV54Gy was increased while the standard deviation was decreased for all cases. Conclusion: Implementation of the AMSO‐FSPB algorithm improved the dosimetric quality of the supine head and neck VMAT plans and was done so with minimal additional cost to clinical resources. This research is supported by CPRIT Individual Investigator Award RP110329.