Purpose: PARETO (Pareto-Aware Radiotherapy Evolutionary Treatment Optimization) is a novel multiobjective treatment planning system that performs beam orientation and fluence optimization simultaneously using an advanced evolutionary algorithm. In order to reduce the number of parameters involved in this enormous search space, we present several methods for modeling the beam fluence. The parameterizations are compared using innovative tools that evaluate fluence complexity, solution quality, and run efficiency.
Methods: A PARETO run is performed using the basic weight (BW), linear gradient (LG), cosine transform (CT), beam group (BG), and isodose-projection (IP) methods for applying fluence modulation over the projection of the Planning Target Volume in the beam's-eye-view plane. The solutions of each run are non-dominated with respect to other trial solutions encountered during the run. However, to compare the solution quality of independent runs, each run competes against every other run in a round robin fashion. Score is assigned based on the fraction of solutions that survive when a tournament selection operator is applied to the solutions of the two competitors. To compare fluence complexity, a modulation index, fractal dimension, and image gradient entropy are calculated for the fluence maps of each optimal plan.
Results: We have found that the LG method results in superior solution quality for a spine phantom, lung patient, and cauda equina patient. The BG method produces solutions with the highest degree of fluence complexity. Most methods result in comparable run times.
Conclusion: The LG method produces superior solution quality using a moderate degree of fluence modulation.
Keywords: Computed tomography; Entropy; Fractals; Lungs; Medical imaging; Medical treatment optimization; Medical treatment planning; Numerical modeling; Radiation therapy.
© 2012 American Association of Physicists in Medicine.