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SU‐E‐T‐376: Incorporating Photon Beamlet Energy in Optimization of Intensity Modulated Beams
Author(s) -
McGeachy P,
Khan R
Publication year - 2013
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 - 10.1118/1.4814810
Subject(s) - imaging phantom , photon , beam (structure) , physics , radiation , intensity (physics) , intensity modulation , dosimetry , energy (signal processing) , matlab , optics , radius , photon energy , radiation therapy , nuclear medicine , computer science , medicine , computer security , quantum mechanics , phase modulation , phase noise , operating system
Purpose: To develop an energy dependent optimization criteria for incorporation into the intensity modulated radiation therapy (IMRT) optimization problem and perform simultaneous optimization of photon energy and intensity for X‐ray modulated radiotherapy (XMRT). Methods: For a simple spherical tumor model, energy dependent optimization criteria (EDOC) were formulated based on three objectives: maximize distal edge dose of the tumor, minimize healthy tissue entrance dose, and maximize uniformity of the tumor dose distribution. This was implemented in MATLAB to test on a simplified scenario of a 10 cm wide 1D photon beam incident on a spherical tumor (10cm radius) centered within a 5600 cm 3 water phantom. The 1D beam was further partitioned into 20 beamlets of equal width. From the MATLAB program, an optimal assignment of monoenergetic photons (ranging from 1 MeV to 18 MeV) for each beamlet based on the EDOC was obtained. The dose calculation for each monoenergetic photon beamlet was restricted to primary dose only. Results: The beamlet distributions showed the mid tumor region assigned higher energies due to the large amount of tumor tissue traversed. Outer edge beamlets were assigned lower energies to spare the large portion of healthy tissue traversed. Changing priorities for each objective effected the optimal distribution in an intuitive manner (i.e. larger emphasis on sparing healthy tissue results in lower energies assigned to each beamlet). Conclusion: A viable set of energy dependent optimization criteria has been developed that can be used in reformulating the inverse optimization problem for radiotherapy, allowing optimization in terms of both beam energy and intensity. Currently, geant4 is being used to generate and benchmark dose profiles for 1 MeV to 18 MeV photons energies to improve the accuracy of the dose calculation. A rigorous testing of the optimization criteria through added complexity of radiotherapy is currently being investigated.