Abstract
The widespread use of 3-D treatment planning has prompted the need for automated techniques to assist the treatment planner in selecting suitable plans. Approaches to treatment plan optimization are based on the presumption that a computer algorithm can rapidly search through the space of beam parameters to determine those parameters that yield the best treatment plan. Procedures for treatment plan optimization consist of two components. The first component is the definition of an objective function that quantifies the desirability of a treatment plan, while the second component consists of an algorithm that maximizes (or minimizes) the objective function. While the latter task is a relatively straightforward problem in mathematical optimization and is limited by execution time and computer memory, the more difficult task is that of defining the “best” treatment plan.