Intensity-Modulated Radiotherapy Optimization in a Quasi-Periodically Deforming Patient Model

Abstract

To present the implementation of a probability-based, four-dimensional (4D) intensity-modulated radiotherapy (IMRT) planning approach that explicitly optimizes the accumulated dose to moving tissue, estimated using the patient's probability density function (pdf) of respiratory motion. This is termed "optimization in tissue's-eye-view". The method incorporates 4D Monte Carlo dose calculation in multiple geometries of a respiratory-correlated CT dataset. The instance doses are weighted according to the breathing pdf and accumulated in a common reference geometry, which involves dose warping based on deformable registration. The algorithm produces deliverable multileaf collimator segments and was tested on a sample lung cancer patient dataset with large target excursion. Accumulated doses of the moving target and organs at risk of this plan were compared with those of corresponding margin-based static IMRT plans for free-breathing and gated treatment, as well as target tracking. Target tracking provided best target coverage. Both the presented 4D IMRT approach for free-breathing treatment and gated treatment gave similar results for target coverage and lung dose, with significantly better target coverage than the margin-based static IMRT plan for free-breathing treatment. The presented 4D planning concept offers an alternative to gating by providing the optimal dose for free-breathing IMRT treatment. Although the focus of this study was 4D lung planning, the approach can be generally applied for IMRT optimization in randomly deforming patient models.