  We apply the recently proposed superiorization methodology (SM) to the
inverse planning problem in radiation therapy. The inverse planning problem is
represented here as a constrained minimization problem of the total variation
(TV) of the intensity vector over a large system of linear two-sided
inequalities. The SM can be viewed conceptually as lying between
feasibility-seeking for the constraints and full-fledged constrained
minimization of the objective function subject to these constraints. It is
based on the discovery that many feasibility-seeking algorithms (of the
projection methods variety) are perturbation-resilient, and can be proactively
steered toward a feasible solution of the constraints with a reduced, thus
superiorized, but not necessarily minimal, objective function value.
