Optimal inverse treatment planning by stochastic continuation

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.