Optimisation in radiotherapy. III: Stochastic optimisation algorithms and conclusions.
This is the final article in a three part examination of optimisation in radiotherapy. Previous articles have established the bases and form of the radiotherapy optimisation problem, and examined certain types of optimisation algorithm, namely, those which perform some form of ordered search of the solution space (mathematical programming), and those which attempt to find the closest feasible solution to the inverse planning problem (deterministic inversion). The current paper examines algorithms which search the space of possible irradiation strategies by stochastic methods. The resulting iterative search methods move about the solution space by sampling random variates, which gradually become more constricted as the algorithm converges upon the optimal solution. This paper also discusses the implementation of optimisation in radiotherapy practice.
Ebert
M
M
Department of Medical Physics, Royal Perth Hospital, Western Australia. martin.ebert@nero.rph.health.wa.gov.au
D000465:Algorithms; D055592:Biophysical Phenomena; D001703:Biophysics; D006801:Humans; D017711:Nonlinear Dynamics; D011880:Radiotherapy Planning, Computer-Assisted / Q000706:statistics & numerical data*; D013269:Stochastic Processes