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Robust Optimization for Radiosurgery under the Static Dose Cloud Approximation
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This report investigates methods of optimization to make treatment plans in radiosurgery robust to spatial uncertainty, and attempts to determine whether they could be used with bene t in a Gamma Knife context. To make the problem mathematically feasible, regions of interest (ROIs) are approximated to move in a pre-computed static dose cloud, which in turn is estimated by methods of linear interpolation and linear approximation.

The movements of ROIs are modeled by transforms, of which rigid, general affine, and a special case of non-affine transforms are treated. Of these, the rigid transforms are used to flexibly assess various properties of the robust optimization model; the a ne transforms to model the total geometric error of the Gamma Knife, excluding ROI delineation; and the non-affine transforms for initial modeling of the important delineation uncertainty.

For the geometric errors, traditionally seen as small for the Gamma Knife, the robust treatment plans are seen to compare favorably to those obtained by non-robust optimization. Delineation errors are found to need careful modeling in order to avoid excessively conservative plans, which may harm normal tissue.

Place, publisher, year, edition, pages
TRITA-MAT-E, 2014:64
National Category
Computational Mathematics
URN: urn:nbn:se:kth:diva-156415OAI: diva2:766647
Subject / course
Optimization and Systems Theory
Educational program
Master of Science in Engineering -Engineering Physics
Available from: 2014-11-28 Created: 2014-11-27 Last updated: 2014-11-28Bibliographically approved

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