Objective: We apply the superiorization methodology to the intensity-modulated
radiation therapy (IMRT) treatment planning problem. In superiorization, linear
voxel dose inequality constraints are the fundamental modeling tool within which a
feasibility-seeking projection algorithm will seek a feasible point. This algorithm is
then perturbed with gradient descent steps to reduce a nonlinear objective function.
Approach: Within 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 its performance with feasibility-seeking and nonlinear constrained
optimization. For these comparisons, we use the TG119 water phantom and a head and-
neck patient of the CORT dataset.
Main Results: Bare feasibility-seeking with AMS confirms previous studies, showing
it can find solutions that are nearly equivalent to those found by the established piecewise
least-squares optimization approach. The superiorization prototype solved the
linearly constrained planning problem with similar performance to that of a general purpose
nonlinear constrained optimizer while showing smooth convergence in both
constraint proximity and objective function reduction.
Significance: Superiorization 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.
Preprint, July 26, 2022.