title: Inverse radiosurgery treatment planning through deconvolution and constrained optimization.
abstract: An inverse radiosurgery treatment planning approach is presented which calculates conformal dose distributions for small, irregularly shaped targets. Two general approaches have been suggested for solution of the inverse radiotherapy problem: explicit and implicit. Explicit methods are typically fast, but generally require geometric and/or dosimetric simplifications. Implicit methods have also been used, but are computationally expensive because they require iterative manipulation of each beam's individual elements. The method presented here incorporates an integrated approach in order to efficiently solve the inverse problem without requiring simplifications which may affect the accuracy of the final result. A deconvolution algorithm (explicit approach) is utilized to determine the intensity modulation function for multiple user-selected beam's eye views of the desired dose distribution. A simulated annealing algorithm (implicit approach) then optimizes each beam's macroscopic weight. Additionally, this method is fully three-dimensional and accurately models phantom scatter by incorporating Monte Carlo generated energy deposition kernels into the dosimetry process. Several small target structure examples are presented and applicability of this methodology to larger targets for general radiotherapy cases is addressed.
authors.affiliation: Keesler Medical Center, Department of Radiation Oncology, Keesler AFB, Mississippi 39534, USA.
authors.forename: J F
authors.initials: JF
authors.lastname: Harmon
authors.forename: F
authors.initials: F
authors.lastname: Bova
authors.forename: S
authors.initials: S
authors.lastname: Meeks
mesh_terms: D000465:Algorithms; D055592:Biophysical Phenomena; D001703:Biophysics; D006801:Humans; D008962:Models, Theoretical; D009010:Monte Carlo Method; D016634:Radiosurgery / Q000379:methods* / Q000706:statistics & numerical data; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data