Fast direct Monte Carlo optimization using the inverse kernel approach

Abstract

The loss of treatment plan quality after segmentation following fluence optimization is a problem in IMRT. In a previous publication we showed that re-optimization helps to re-establish part of the plan quality. Recently the so-called direct aperture optimization method has been introduced to successfully overcome that difficulty. The aim of the present paper is to present in detail the integration of the inverse kernel method into direct aperture optimization. It can be shown that this integration leads to a system with high performance with regard to time, while Monte Carlo precision is maintained. The integrated simulated annealing optimization algorithm allows easy adaptation to any multi-leaf collimator and it is open to any complex objective function. Investigations of simulated annealing control parameters are performed to improve the performance. The system denoted by direct Monte Carlo optimization (DMCO) is demonstrated on the Carpet phantom and a clinical prostate case as well. Results are compared to inverse kernel optimizations, showing a remarkable time reduction and simultaneously an improvement in plan quality for the Carpet phantom.