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Automated improvement of radiation therapy treatment plans by optimization under reference dose constraints
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
2012 (English)In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 57, no 23, 7799-7811 p.Article in journal (Refereed) Published
Abstract [en]

A method is presented that automatically improves upon previous treatment plans by optimization under reference dose constraints. In such an optimization, a previous plan is taken as reference and a new optimization is performed toward some goal, such as minimization of the doses to healthy structures under the constraint that no structure can become worse off than in the reference plan. Two types of constraints that enforce this are discussed: either each voxel or each dose-volume histogram of the improved plan must be at least as good as in the reference plan. These constraints ensure that the quality of the dose distribution cannot deteriorate, something that constraints on conventional physical penalty functions do not. To avoid discontinuous gradients, which may restrain gradient-based optimization algorithms, the positive part operators that constitute the optimization functions are regularized. The method was applied to a previously optimized plan for a C-shaped phantom and the effects of the choice of regularization parameter were studied. The method resulted in reduced integral dose and reduced doses to the organ at risk while maintaining target homogeneity. It could be used to improve upon treatment plans directly or as a means of quality control of plans.

Place, publisher, year, edition, pages
2012. Vol. 57, no 23, 7799-7811 p.
Keyword [en]
Imrt, Penalty, Prescription, Radiotherapy, Quality
National Category
Medical and Health Sciences
URN: urn:nbn:se:kth:diva-109170DOI: 10.1088/0031-9155/57/23/7799ISI: 000311351400010ScopusID: 2-s2.0-84870373457OAI: diva2:580390
Swedish Research Council

QC 20121221

Available from: 2012-12-21 Created: 2012-12-21 Last updated: 2013-05-16Bibliographically approved
In thesis
1. Robust optimization of radiation therapy accounting for geometric uncertainty
Open this publication in new window or tab >>Robust optimization of radiation therapy accounting for geometric uncertainty
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Geometric errors may compromise the quality of radiation therapy treatments. Optimization methods that account for errors can reduce their effects.

The first paper of this thesis introduces minimax optimization to account for systematic range and setup errors in intensity-modulated proton therapy. The minimax method optimizes the worst case outcome of the errors within a given set. It is applied to three patient cases and shown to yield improved target coverage robustness and healthy structure sparing compared to conventional methods using margins, uniform beam doses, and density override. Information about the uncertainties enables the optimization to counterbalance the effects of errors.

In the second paper, random setup errors of uncertain distribution---in addition to the systematic range and setup errors---are considered in a framework that enables scaling between expected value and minimax optimization. Experiments on a phantom show that the best and mean case tradeoffs between target coverage and critical structure sparing are similar between the methods of the framework, but that the worst case tradeoff improves with conservativeness.

Minimax optimization only considers the worst case errors. When the planning criteria cannot be fulfilled for all errors, this may have an adverse effect on the plan quality. The third paper introduces a method for such cases that modifies the set of considered errors to maximize the probability of satisfying the planning criteria. For two cases treated with intensity-modulated photon and proton therapy, the method increased the number of satisfied criteria substantially. Grasping for a little less sometimes yields better plans.

In the fourth paper, the theory for multicriteria optimization is extended to incorporate minimax optimization. Minimax optimization is shown to better exploit spatial information than objective-wise worst case optimization, which has previously been used for robust multicriteria optimization.

The fifth and sixth papers introduce methods for improving treatment plans: one for deliverable Pareto surface navigation, which improves upon the Pareto set representations of previous methods; and one that minimizes healthy structure doses while constraining the doses of all structures not to deteriorate compared to a reference plan, thereby improving upon plans that have been reached with too weak planning goals.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2013. xvii, 39 p.
Trita-MAT. OS, ISSN 1401-2294 ; 13:06
Optimization, intensity-modulated proton therapy, uncertainty, robust planning, setup error, range error, intensity-modulated radiation therapy, multicriteria optimization
National Category
urn:nbn:se:kth:diva-122262 (URN)978-91-7501-771-6 (ISBN)
Public defence
2013-06-05, F3, Lindstedtsvägen 26, KTH, Stockholm, 10:00 (English)
Swedish Research Council, VR 2007-4794

QC 20130516

Available from: 2013-05-16 Created: 2013-05-15 Last updated: 2013-05-16Bibliographically approved

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