ArticleMedical physics2017
Deterministic direct aperture optimization using multiphase piecewise constant segmentation.
Article in Medical physics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Reformulated McNamara RBE-weighted beam orientation optimization for intensity modulated proton therapy.Medical physics · 2022Article
- Dose prediction with deep learning for prostate cancer radiation therapy: Model adaptation to different treatment planning practices.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2020Article
- Using deep learning to predict beam-tunable Pareto optimal dose distribution for intensity-modulated radiation therapy.Medical physics · 2020Article
- Article
- Many-isocenter optimization for robotic radiotherapy.Physics in medicine and biology · 2020Article
- A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning.Scientific reports · 2019Article
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Authors and funding
4 authors.
Funding
Abstract
purposeDirect aperture optimization (DAO) attempts to incorporate machine constraints in the inverse optimization to eliminate the post-processing steps in fluence map optimization (FMO) that degrade plan quality. Current commercial DAO methods utilize a stochastic or greedy approach to search a small aperture solution space. In this study, we propose a novel deterministic direct aperture optimization that integrates the segmentation of fluence map in the optimization problem using the multiphase piecewise constant Mumford-Shah formulation.
methodsThe Mumford-Shah based direct aperture optimization problem was formulated to include an L2-norm dose fidelity term to penalize differences between the projected dose and the prescribed dose, an anisotropic total variation term to promote piecewise continuity in the fluence maps, and the multiphase piecewise constant Mumford-Shah function to partition the fluence into pairwise discrete segments. A proximal-class, first-order primal-dual solver was implemented to solve the large scale optimization problem, and an alternating module strategy was implemented to update fluence and delivery segments. Three patients of varying complexity-one glioblastoma multiforme (GBM) patient, one lung (LNG) patient, and one bilateral head and neck (H&N) patient with 3 PTVs-were selected to test the new DAO method. For each patient, 20 non-coplanar beams were first selected using column generation, followed by the Mumford-Shah based DAO (DAO
resultsDAO
conclusionsA novel deterministic direct aperture optimization formulation was developed and evaluated. It combines fluence map optimization and the multiphase piecewise constant Mumford-Shah segmentation into a unified framework, and the resulting optimization problem can be solved efficiently. Compared to the widely and commercially used simulated annealing DAO approach, it showed comparable dosimetry behavior for simple plans, and substantially improved OAR sparing, PTV coverage, PTV homogeneity, high dose spillage, and conformity for the more complex head and neck plan.
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