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Sci‐AM2 Sat ‐ 02: Energy optimization for inverse planning
Author(s) -
Sévigny C,
Beaulieu L,
Beaulieu F
Publication year - 2005
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.2031052
Subject(s) - aperture (computer memory) , simulated annealing , radiation treatment planning , energy (signal processing) , inverse , photon , computer science , mathematical optimization , optics , algorithm , mathematics , physics , acoustics , surgery , radiation therapy , statistics , medicine , geometry
Purpose: To simultaneously optimize the photon beam energy, orientations(gantry, table), wedge filters and beam weights in aperture‐based IMRT, a new inverse planning system has been developed. This system, Ballista , uses anatomy‐based MLC fields as aperture. It is an alternative for beamlet‐based IMRT. The aim of this study is to include the photon energy in the optimization process. Method and Materials: Ballista uses a fast simulated annealing algorithm to select the optimal configuration of beams with respect to the objectives specified by the planner. To include the energy as a free parameter in the existing overall optimization system, this variable is interpreted by the algorithm as an angle. That is, each eligible energy is assigned to a circular section of the space of all possible solutions. In addition to varying the energy of the individual beams, an option was added to the process which allows associating a combination of energies to various segments of a single field. Results: The effectiveness of energy optimization has been tested on several sites. The lung cancer has provided good results. The value of V 20 (29.6%vs.35%) is reduced as well as the mean dose to the heart(14.5%vs.21.5%). Also, a better tumor coverage (V95%=97.8%vs.96.6% and IH=1.07vs.1.10) is obtained in the case of a non‐coplanar plan realized with this new approach in comparison with a standard plan (23MV only). Conclusion: The inclusion of energy gives the optimization process an additional advantage over standard planning, and better close the gap between the optimized approach and the clinical treatment methods.