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BEST IN PHYSICS (THERAPY) - 4D IMRT Planning Using Highly-Parallelizable Particle Swarm Optimization

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A Modiri

A Modiri*, X Gu and A Sawant , UT Southwestern Medical Center, Dallas, TX

Presentations

TH-A-9A-2 Thursday 7:30AM - 9:30AM Room: 9A

Purpose:
We present a particle swarm optimization (PSO)-based 4D IMRT planning technique designed for dynamic MLC tracking delivery to lung tumors. The key idea is to utilize the temporal dimension as an additional degree of freedom rather than a constraint in order to achieve improved sparing of organs at risk (OARs).

Methods:
The target and normal structures were manually contoured on each of the ten phases of a 4DCT scan acquired from a lung SBRT patient who exhibited 1.5cm tumor motion despite the use of abdominal compression. Corresponding ten IMRT plans were generated using the Eclipse treatment planning system. These plans served as initial guess solutions for the PSO algorithm. Fluence weights were optimized over the entire solution space i.e., 10 phases x 12 beams x 166 control points. The size of the solution space motivated our choice of PSO, which is a highly parallelizable stochastic global optimization technique that is well-suited for such large problems. A summed fluence map was created using an in-house B-spline deformable image registration. Each plan was compared with a corresponding, internal target volume (ITV)-based IMRT plan.

Results:
The PSO 4D IMRT plan yielded comparable PTV coverage and significantly higher dose–sparing for parallel and serial OARs compared to the ITV-based plan. The dose-sparing achieved via PSO-4DIMRT was: lung Dmean = 28%; lung V20 = 90%; spinal cord Dmax = 23%; esophagus Dmax = 31%; heart Dmax = 51%; heart Dmean = 64%.

Conclusion:
Truly 4D IMRT that uses the temporal dimension as an additional degree of freedom can achieve significant dose sparing of serial and parallel OARs. Given the large solution space, PSO represents an attractive, parallelizable tool to achieve globally optimal solutions for such problems. This work was supported through funding from the National Institutes of Health and Varian Medical Systems.


Funding Support, Disclosures, and Conflict of Interest: Amit Sawant has research funding from Varian Medical Systems, VisionRT Ltd. and Elekta

Handouts


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