Influence of Sampling in Radiation Therapy Treatment Design
Computer-based optimization simulations have made significant contributions to the improvement of intensity modulated radiation therapy (IMRT) treatment planning. Large amounts of data are typically involved in radiation therapy optimization problems. Regardless the formulation used, the problem size is always the biggest challenge to overcome. The most common strategy to address this problem is sampling which may have a significant impact on the quality of the results. There are few studies on sampling for optimization in radiation therapy, mostly devoted to propose new sampling approaches that accelerate IMRT optimization. However, the gain in computational time comes at a cost: as sampling becomes progressively coarse, the quality of the solution deteriorates. A clinical example of a head and neck case is used to discuss the influence of sampling in radiation therapy treatment design, emphasizing the influence on parotid sparing. Procedures on the choice of the most adequate sample rate are highlighted.
KeywordsOR in medicine radiotherapy sampling mathematical models optimization inverse planning
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