Superiorization of projection algorithms for linearly constrained inverse radiotherapy treatment planning

Superiorization of projection algorithms for linearly constrained inverse radiotherapy treatment planning

Blog Article

ObjectiveWe apply the superiorization methodology to the constrained intensity-modulated radiation therapy (IMRT) treatment planning problem.Superiorization combines a feasibility-seeking projection algorithm with objective function reduction: The underlying projection algorithm is perturbed with gradient descent steps to steer the algorithm towards a solution with a lower objective function value compared to RAW TURMERIC GOLD HONEY one obtained solely through feasibility-seeking.ApproachWithin the open-source inverse planning toolkit matRad, we implement a prototypical algorithmic framework for superiorization using the well-established Agmon, Motzkin, and Schoenberg (AMS) feasibility-seeking projection algorithm and common nonlinear dose optimization objective functions.Based on this prototype, we apply superiorization to intensity-modulated radiation therapy treatment planning and compare it with (i) bare feasibility-seeking (i.e.

, without any objective function) and (ii) nonlinear constrained optimization using first-order derivatives.For these comparisons, we use the TG119 water phantom, the head-and-neck and the prostate patient of the CORT dataset.Main resultsBare feasibility-seeking with AMS confirms previous studies, showing it can Immune Support find solutions that are nearly equivalent to those found by the established piece-wise least-squares optimization approach.The superiorization prototype solved the linearly constrained planning problem with similar dosimetric performance to that of a general-purpose nonlinear constrained optimizer while showing smooth convergence in both constraint proximity and objective function reduction.SignificanceSuperiorization is a useful alternative to constrained optimization in radiotherapy inverse treatment planning.

Future extensions with other approaches to feasibility-seeking, e.g., with dose-volume constraints and more sophisticated perturbations, may unlock its full potential for high performant inverse treatment planning.