Accelerated robust optimization algorithm for proton therapy treatment planning

Medical Physics
Gregory ButiEdmond Sterpin

Abstract

Robust optimization is a computational expensive process resulting in long plan computation times. This issue is especially critical for moving targets as these need a large number of uncertainty scenarios to robustly optimize their treatment plans. In this study, we propose a novel worst-case robust optimization algorithm, called dynamic minimax, that accelerates the conventional minimax optimization. Dynamic minimax optimization aims at speeding up the plan optimization process by decreasing the number of evaluated scenarios in the optimization. For a given pool of scenarios (for instance 63 = 7 setup × 3 range × 3 breathing phases), the proposed dynamic minimax algorithm only considers a reduced number of candidate-worst scenarios, selected from the full 63 scenario set. These scenarios are updated throughout the optimization by randomly sampling new scenarios according to a hidden variable P, called the `probability acceptance function', which associates with each scenario the probability of it being selected as the worst case. By doing so, the algorithm favors scenarios that are mostly "active", that is, frequently evaluated as the worst case. Additionally, unconsidered scenarios have the possibility to be re-considered, l...Continue Reading

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Citations

Jul 9, 2021·Physics in Medicine and Biology·Gregory ButiEdmond Sterpin
Aug 6, 2021·Radiotherapy and Oncology : Journal of the European Society for Therapeutic Radiology and Oncology·Jesús Rojo-SantiagoMischa S Hoogeman

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