Division of Optimization and Systems Theory

Department of Mathematics

KTH

He will present his thesis that will be defended June 14:

Treatment planning for radiation therapy inherently involves tradeoffs, such as between tumor control and normal tissue sparing, between time-efficiency and dose quality, and between nominal plan quality and robustness. The purpose of this thesis is to develop methods that can facilitate decision making related to such tradeoffs. The main focus of the thesis is on a form of multicriteria optimization where the clinical plan is selected by continuous interpolation over a set of precalculated alternatives. The precalculated treatment plans constitute a subset of the set of Pareto optimal plans, meaning plans such that no criterion can be improved without a sacrifice in another criterion.
Approximation of the Pareto optimal set is first studied with respect to fluence map optimization for intensity-modulated radiation therapy. The approximation error of a discrete Pareto set representation is minimized by an algorithm that generates Pareto points one at the time at the location where the distance between an inner and outer approximation of the Pareto optimal set currently attains its maximum. A technique for calculating this distance that is orders of magnitude more efficient than the best previous method is presented. A generalization of the approximation method to distributed computational environments is also proposed.
Approximation of the Pareto optimal set is also considered with respect to direct machine parameter optimization. Optimization of this form is used to calculate representations where any interpolated treatment plan is directly deliverable. The fact that finite Pareto optimal set representations have approximation errors with respect to Pareto optimality is addressed by a technique that removes these errors by a projection onto the exact Pareto optimal set. Projections are also studied subject to constraints that prevent the dose-volume histogram from deteriorating.
Multicriteria optimization is extended to treatment planning for volumetric-modulated arc therapy and intensity-modulated proton therapy. Proton therapy plans that are robust against geometric errors are calculated by optimization of the worst case outcome, and the theory for multicriteria optimization is extended to accommodate this formulation. Worst case optimization is shown to be preferable to a previous more conservative method that also protects against uncertainties which cannot be realized in practice.

Calendar of seminars