Michalski Darek, Xiao Ying, Censor Yair, Galvin James M
Department of Radiation Oncology, Kimmel Cancer Center, Jefferson Medical College of Thomas Jefferson University, 111 South 11th Street, Philadelphia, PA 19107, USA.
Phys Med Biol. 2004 Feb 21;49(4):601-16. doi: 10.1088/0031-9155/49/4/010.
The prescribed goals of radiation treatment planning are often expressed in terms of dose-volume constraints. We present a novel formulation of a dose-volume constraint satisfaction search for the discretized radiation therapy model. This approach does not rely on any explicit cost function. Inverse treatment planning uses the aperture-based approach with predefined, according to geometric rules, segmental fields. The solver utilizes the simultaneous version of the cyclic subgradient projection algorithm. This is a deterministic iterative method designed for solving the convex feasibility problems. A prescription is expressed with the set of inequalities imposed on the dose at the voxel resolution. Additional constraint functions control the compliance with selected points of the expected cumulative dose-volume histograms. The performance of this method is tested on prostate and head-and-neck cases. The relationships with other models and algorithms of similar conceptual origin are discussed. The demonstrated advantages of the method are: the equivalence of the algorithmic and prescription parameters, the intuitive setup of free parameters, and the improved speed of the method as compared to similar iterative as well as other techniques. The technique reported here will deliver approximate solutions for inconsistent prescriptions.
