1. Optimal inverse treatment planning by stochastic continuation
- Author
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F. Smekens, Marc Robini, Bruno Sixou, Imagerie et modélisation Vasculaires, Thoraciques et Cérébrales (MOTIVATE), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Imagerie Tomographique et Radiothérapie
- Subjects
Mathematical optimization ,nouvelleₑquipe₇ ,Generalization ,Stochastic process ,Markov process ,Function (mathematics) ,modelisationₚb_inverses ,symbols.namesake ,Continuation ,Convergence (routing) ,Simulated annealing ,[INFO.INFO-IM]Computer Science [cs]/Medical Imaging ,symbols ,categₘixte ,Imagerie tomographique et thérapie par rayonnement ,Global optimization ,ComputingMilieux_MISCELLANEOUS ,Mathematics - Abstract
Simulated annealing (SA) is a well-known optimal approach to global optimization which is often used in inverse treatment planning. However, SA generally converges very slowly and many acceleration techniques have been proposed at the expense of a loss of theoretical convergence properties. In this paper, we investigate a recently proposed generalization of SA for dose optimization. This class of algorithms, called stochastic continuation (SC), is theoretically grounded and introduces substantial flexibility in the design of annealing-based methods; simply speaking, SC is a variant SA in which both the generation mechanism and the energy function are allowed to be time-dependent. We propose an SC approach to particle therapy that can be easily applied to a large class of inverse treatment planning problems. Numerical experiments indicate that it outperforms SA both qualitatively and quantitatively.
- Published
- 2011