A two‐phase method for selecting IMRT treatment beam angles: Branch‐and‐Prune and local neighborhood search

A two‐phase method for selecting IMRT treatment beam angles: Branch‐and‐Prune and local neighborhood search

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Article ID: iaor201111540
Volume: 217
Issue: 3
Start Page Number: 609
End Page Number: 618
Publication Date: Mar 2012
Journal: European Journal of Operational Research
Authors: ,
Keywords: combinatorial optimization, programming: branch and bound, heuristics: local search
Abstract:

This paper presents a new two‐phase solution approach to the beam angle and fluence map optimization problem in Intensity Modulated Radiation Therapy (IMRT) planning. We introduce BranchandPrune (B&P) to generate a robust feasible solution in the first phase. A local neighborhood search algorithm is developed to find a local optimal solution from the Phase I starting point in the second phase. The goal of the first phase is to generate a clinically acceptable feasible solution in a fast manner based on a Branch‐and‐Bound tree. In this approach, a substantially reduced search tree is iteratively constructed. In each iteration, a merit score based branching rule is used to select a pool of promising child nodes. Then pruning rules are applied to select one child node as the branching node for the next iteration. The algorithm terminates when we obtain a desired number of angles in the current node. Although Phase I generates quality feasible solutions, it does not guarantee optimality. Therefore, the second phase is designed to converge Phase I starting solutions to local optimality. Our methods are tested on two sets of real patient data. Results show that not only can B&P alone generate clinically acceptable solutions, but the two‐phase method consistently generates local optimal solutions, some of which are shown to be globally optimal.

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