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Initial Results of VMAT Re-Planning for On-Line Adaptive Radiotherapy


L Jiang

L Jiang1*, V Kearney1, Z Zhong2, J Yordy1, S Chen1, L Nedzi1, T Solberg1, W Mao1, (1) UT Southwestern Medical Center, Dallas, TX, (2) UT Dallas, Richardson, Texas,

SU-E-T-629 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Purpose: The volumetric-modulated arc therapy (VMAT) has been widely used due to its fast delivery and good dose confirmation to target. Adaptive radiation therapy becomes essential to address the anatomic changes during a course of radiotherapy. A fast algorithm to re-plan treatment has been investigated to adapt the changes of targets and OARs.
Methods: All studies were based on data of head/neck cancer patients treated by VMAT in our institution. These patients had second CT scans, contours, and treatment plans in the middle of the treatment course. CT image datasets, ROI structures, and arc beam apertures were exported from a commercial treatment planning system (Pinnacle v9.0, Philips Radiation Oncology Systems, Madison, WI). The beam apertures were adjusted based on volume change of targets and OARs volume contoured based on second CT scans. Every beam segment was loaded to Eclipse treatment plan system (Varian Medical Systems, Palo Alto, CA) to calculate dose distribution of individual beam separately. The original planning dosimetric restrictions were recalculated as individual cost functions and their weighted summation formed an overall cost function. A Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm was applied to optimize the weighting factor of every segment by minimizing the overall cost function.
Results: Data of three patients were used. Three plans with 2 to 4 arcs (178 ~ 256 beams) were re-planned. Although 15 to 17 cost functions were involved, high quality of VMAT plans were obtained within a short time.
Conclusions: This initial research demonstrates the feasibility of on-line VMAT re-planning for adaptive radiotherapy. Though this initial work focuses on optimization of arc beams weight, it can be intergraded with on-board imaging and GPU calculating technique for online adaptive radiotherapy system which include automatic reconstruction of on-board images, deformable image registration, dose evaluation, apertures adjustment, dose calculation, re-optimization, and MLC sequencing.


Funding Support, Disclosures, and Conflict of Interest: This research is supported by CPRIT Individual Investigator Award RP110329.

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