Invited Session Wed.3.MA 376

Wednesday, 15:15 - 16:45 h, Room: MA 376

Cluster 12: Life sciences & healthcare [...]

Radiation therapy treatment planning


Chair: Edwin Romeijn



Wednesday, 15:15 - 15:40 h, Room: MA 376, Talk 1

Troy Campbell Long
Beam orientation optimization in radiation therapy treatment planning

Coauthor: Hilbrand Edwin Romeijn


In beam orientation optimization, a small number of beam positions must be selected that allow for both a high treatment plan quality and efficient deliverability, creating a large-scale combinatorial optimization problem. Our goal is to develop an efficient and effective method for selecting high-quality coplanar or non-coplanar beam orientations for IMRT treatments that explicitly incorporates the effect of this selection on the quality of the resulting optimal dose distribution. To this end, we propose a greedy heuristic for solving a model that integrates beam selection with the so-called fluence map optimization problem (which optimizes the dose distribution given a fixed set of beams). The algorithm iteratively adds beams to the model according to a dynamically updated attractiveness measure for each remaining candidate beam. We consider measures that are based explicitly on the optimal dose distribution corresponding to the currently selected set of beams. Several specific attractiveness measures are proposed that use either first-order or both first and second-order information. Performance of the algorithm was assessed on clinical data.



Wednesday, 15:45 - 16:10 h, Room: MA 376, Talk 2

Albin Fredriksson
A characterization of robust radiation therapy optimization methods


A key aspect of external beam radiation therapy is the collocation of the patient anatomy and the treatment beams. Unless accounted for, systematic and random errors risk degrading the delivered treatment severely compared to the planned. We consider a minimax stochastic optimization framework that generalizes many previous methods used to account for systematic and random errors. The methods within this framework range from expected value to worst case optimization. We characterize how methods of this framework yield robust plans by studying the results of applying them to a two-dimensional phantom, and discuss how they differ from a conventional method that uses margins to account for uncertainties. For random errors, the importance of taking into account uncertainty in the probability distribution of the errors is highlighted.



Wednesday, 16:15 - 16:40 h, Room: MA 376, Talk 3

Marina A. Epelman
A column generation-based algorithm for Volumetric Modulated Arc Therapy (VMAT) treatment plan optimization

Coauthors: Xiejun Gu, Xun Jia, Steve B. Jiang, Fei Peng, Edwin Romeijn


External beam radiation therapy is a common treatment for many types of cancer. During such treatment, radiation is delivered with a gantry, equipped with a radiation source, that is pointed at the patient from various angles. Optimization models are commonly used in individualized treatment planning, and formulation and solution methods for such models are an area of active research. VMAT is a particular technique for delivering radiation, in which the gantry continuously rotates around the patient while the leaves of a multi-leaf collimator (MLC) move in and out of the radiation field to shape it. This technique has the potential to produce treatments of high quality similar to, e.g., Intensity Modulated Radiation Therapy (IMRT), but requiring less time for delivery. Recently, comercial systems capable of delivering VMAT treatments became available, necessitating the development of relevant treatment planning methods. We propose one such method, which uses optimization models and column generation-based heuristics to produce high-quality VMAT treatment plans that allow for dynamically adjustable gantry speed and dose rate, and MLC leaf speed constraints.


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