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Sampling Issues for Optimization in Radiotherapy

Michael C. Ferris
University of Wisconsin, Madison, Wisconsin, USA
Computer Sciences Department

Thursday, September 29, 2005
JEC 3117 - 4:00 p.m. to 5:00 p.m.
Refreshments at 3:30 p.m.


A wide variety of optimization problems arise in radiation treatment planning. Many different optimization techniques can be applied to their solution, ranging from simulated annealing to mixed integer (non)linear programming. These problems typically involve large amounts of data, derived from simulations of patient anatomy and the properties of the delivery device.

We investigate a three phase approach for the solution of these optimization problems, based on sampling the underlying data. As a particular example, we show how our approach determines optimal beam angles, wedge orientations and delivery intensities in several 3D conformal radiation therapy patient examples, and show the applicability of the approach to a large collection of radiation treatment problems, including IMRT. In our example context, Phase I uses a coarse sampling of the data and determines a collection of promising angles to use. Phase II refines the sampling, and solves a modified problem using only the promising angles. Phase III does a further refinement to the sampling, and fixes most of the discrete decision variables to reduce computation times.

The use of resampling of particular organ structures in this context will be outlined. Particular emphasis will be on general principles that are applicable to large classes of treatment planning problems. Specific examples will also be detailed showing enormous increase in speed of planning, without detriment to the quality of solutions found.

This represents joint work with R. Einarsson (ILOG), Z. Jiang and D. Shepard (Maryland).

Hosted by: Jong-Shi Pang (x2994) and Daniel Freedman (x4785)

Last updated: September 16, 2005