Optimization of inverse treatment planning using a fuzzy weight function

A fuzzy approach has been applied to inverse treatment planning optimization in radiation therapy. The proposed inverse-planning algorithm optimizes both the intensity-modulated beam (LMB) and the normal tissue prescription. In the LMB optimization, we developed a fast-monotonic-descent (FMD) method that has the property of fast and monotonic convergence to the minimum for a constrained quadratic objective function. In addition, a fuzzy weight function is employed to express the vague knowledge about the importance of matching the calculated dose to the prescribed dose in the normal tissue. Then, a validity function is established to optimize the normal tissue prescription. The performance of this new fuzzy prescription algorithm has been compared to that based on hard prescription methods for two treatment geometries. The FMD method presented here both provides a full-analytical solution to the optimization of intensity-modulated beams, and guarantees fast and monotonic convergence to the minimum. It has been shown that the fuzzy inverse planning technique is capable of achieving an optimal balance between the objective of matching the calculated dose to the prescribed dose for the target volume and the objective of minimizing the normal tissue dose. (C) 2000 American Association of Physicists in Medicine.