title: A surrogate-based metaheuristic global search method for beam angle selection in radiation treatment planning.
abstract: An important element of radiation treatment planning for cancer therapy is the selection of beam angles (out of all possible coplanar and non-coplanar angles in relation to the patient) in order to maximize the delivery of radiation to the tumor site and minimize radiation damage to nearby organs-at-risk. This category of combinatorial optimization problem is particularly difficult because direct evaluation of the quality of treatment corresponding to any proposed selection of beams requires the solution of a large-scale dose optimization problem involving many thousands of variables that represent doses delivered to volume elements (voxels) in the patient. However, if the quality of angle sets can be accurately estimated without expensive computation, a large number of angle sets can be considered, increasing the likelihood of identifying a very high quality set. Using a computationally efficient surrogate beam set evaluation procedure based on single-beam data extracted from plans employing equallyspaced beams (eplans), we have developed a global search metaheuristic process based on the nested partitions framework for this combinatorial optimization problem. The surrogate scoring mechanism allows us to assess thousands of beam set samples within a clinically acceptable time frame. Tests on difficult clinical cases demonstrate that the beam sets obtained via our method are of superior quality.
authors: Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA. hzhan001@umaryland.edu
authors: H H
authors: HH
authors: Zhang
authors: S
authors: S
authors: Gao
authors: W
authors: W
authors: Chen
authors: L
authors: L
authors: Shi
authors: W D
authors: WD
authors: D'Souza
authors: R R
authors: RR
authors: Meyer
mesh_terms: D000465:Algorithms; D006258:Head and Neck Neoplasms / Q000532:radiotherapy; D006801:Humans; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*