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Title: PARETO: A novel evolutionary optimization approach to multiobjective IMRT planning

Abstract

Purpose: In radiation therapy treatment planning, the clinical objectives of uniform high dose to the planning target volume (PTV) and low dose to the organs-at-risk (OARs) are invariably in conflict, often requiring compromises to be made between them when selecting the best treatment plan for a particular patient. In this work, the authors introduce Pareto-Aware Radiotherapy Evolutionary Treatment Optimization (pareto), a multiobjective optimization tool to solve for beam angles and fluence patterns in intensity-modulated radiation therapy (IMRT) treatment planning. Methods: pareto is built around a powerful multiobjective genetic algorithm (GA), which allows us to treat the problem of IMRT treatment plan optimization as a combined monolithic problem, where all beam fluence and angle parameters are treated equally during the optimization. We have employed a simple parameterized beam fluence representation with a realistic dose calculation approach, incorporating patient scatter effects, to demonstrate feasibility of the proposed approach on two phantoms. The first phantom is a simple cylindrical phantom containing a target surrounded by three OARs, while the second phantom is more complex and represents a paraspinal patient. Results: pareto results in a large database of Pareto nondominated solutions that represent the necessary trade-offs between objectives. The solution quality was examined formore » several PTV and OAR fitness functions. The combination of a conformity-based PTV fitness function and a dose-volume histogram (DVH) or equivalent uniform dose (EUD) -based fitness function for the OAR produced relatively uniform and conformal PTV doses, with well-spaced beams. A penalty function added to the fitness functions eliminates hotspots. Comparison of resulting DVHs to those from treatment plans developed with a single-objective fluence optimizer (from a commercial treatment planning system) showed good correlation. Results also indicated that pareto shows promise in optimizing the number of beams. Conclusions: This initial evaluation of the evolutionary optimization software tool pareto for IMRT treatment planning demonstrates feasibility and provides motivation for continued development. Advantages of this approach over current commercial methods for treatment planning are many, including: (1) fully automated optimization that avoids human controlled iterative optimization and potentially improves overall process efficiency, (2) formulation of the problem as a true multiobjective one, which provides an optimized set of Pareto nondominated solutions refined over hundreds of generations and compiled from thousands of parameter sets explored during the run, and (3) rapid exploration of the final nondominated set accomplished by a graphical interface used to select the best treatment option for the patient.« less

Authors:
; ; ; ;  [1];  [2];  [2];  [2];  [2]
  1. Department of Physics and Astronomy, University of Manitoba, Winnipeg, Manitoba R3T 2N2 (Canada)
  2. (Canada)
Publication Date:
OSTI Identifier:
22098623
Resource Type:
Journal Article
Journal Name:
Medical Physics
Additional Journal Information:
Journal Volume: 38; Journal Issue: 9; Other Information: (c) 2011 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-2405
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; ALGORITHMS; BEAMS; CHARGES; COMPARATIVE EVALUATIONS; CORRELATIONS; DOSIMETRY; EFFICIENCY; HUMAN POPULATIONS; INTERFACES; ITERATIVE METHODS; MATHEMATICAL SOLUTIONS; OPTIMIZATION; PHANTOMS; PLANNING; RADIOTHERAPY; TOOLS

Citation Formats

Fiege, Jason, McCurdy, Boyd, Potrebko, Peter, Champion, Heather, Cull, Andrew, Department of Physics and Astronomy, University of Manitoba Winnipeg, Manitoba R3T 2N2, Canada Division of Medical Physics, CancerCare Manitoba, Winnipeg, Manitoba R3E 0V9, Canada and Department of Radiology, University of Manitoba, Winnipeg, Manitoba R3T 2N2, Kleysen Institute for Advanced Medicine, Winnipeg Health Sciences Centre, Winnipeg, Manitoba R3A 1R9, Department of Physics and Astronomy, University of Manitoba Winnipeg, Manitoba R3T 2N2, Canada and Division of Medical Physics, CancerCare Manitoba, Winnipeg, Manitoba R3E 0V9, and Department of Physics and Astronomy, University of Manitoba Winnipeg, Manitoba R3T 2N2, Canada and Division of Medical Physics, CancerCare Manitoba, Winnipeg, Manitoba R3E 0V9. PARETO: A novel evolutionary optimization approach to multiobjective IMRT planning. United States: N. p., 2011. Web. doi:10.1118/1.3615622.
Fiege, Jason, McCurdy, Boyd, Potrebko, Peter, Champion, Heather, Cull, Andrew, Department of Physics and Astronomy, University of Manitoba Winnipeg, Manitoba R3T 2N2, Canada Division of Medical Physics, CancerCare Manitoba, Winnipeg, Manitoba R3E 0V9, Canada and Department of Radiology, University of Manitoba, Winnipeg, Manitoba R3T 2N2, Kleysen Institute for Advanced Medicine, Winnipeg Health Sciences Centre, Winnipeg, Manitoba R3A 1R9, Department of Physics and Astronomy, University of Manitoba Winnipeg, Manitoba R3T 2N2, Canada and Division of Medical Physics, CancerCare Manitoba, Winnipeg, Manitoba R3E 0V9, & Department of Physics and Astronomy, University of Manitoba Winnipeg, Manitoba R3T 2N2, Canada and Division of Medical Physics, CancerCare Manitoba, Winnipeg, Manitoba R3E 0V9. PARETO: A novel evolutionary optimization approach to multiobjective IMRT planning. United States. doi:10.1118/1.3615622.
Fiege, Jason, McCurdy, Boyd, Potrebko, Peter, Champion, Heather, Cull, Andrew, Department of Physics and Astronomy, University of Manitoba Winnipeg, Manitoba R3T 2N2, Canada Division of Medical Physics, CancerCare Manitoba, Winnipeg, Manitoba R3E 0V9, Canada and Department of Radiology, University of Manitoba, Winnipeg, Manitoba R3T 2N2, Kleysen Institute for Advanced Medicine, Winnipeg Health Sciences Centre, Winnipeg, Manitoba R3A 1R9, Department of Physics and Astronomy, University of Manitoba Winnipeg, Manitoba R3T 2N2, Canada and Division of Medical Physics, CancerCare Manitoba, Winnipeg, Manitoba R3E 0V9, and Department of Physics and Astronomy, University of Manitoba Winnipeg, Manitoba R3T 2N2, Canada and Division of Medical Physics, CancerCare Manitoba, Winnipeg, Manitoba R3E 0V9. Thu . "PARETO: A novel evolutionary optimization approach to multiobjective IMRT planning". United States. doi:10.1118/1.3615622.
@article{osti_22098623,
title = {PARETO: A novel evolutionary optimization approach to multiobjective IMRT planning},
author = {Fiege, Jason and McCurdy, Boyd and Potrebko, Peter and Champion, Heather and Cull, Andrew and Department of Physics and Astronomy, University of Manitoba Winnipeg, Manitoba R3T 2N2, Canada Division of Medical Physics, CancerCare Manitoba, Winnipeg, Manitoba R3E 0V9, Canada and Department of Radiology, University of Manitoba, Winnipeg, Manitoba R3T 2N2 and Kleysen Institute for Advanced Medicine, Winnipeg Health Sciences Centre, Winnipeg, Manitoba R3A 1R9 and Department of Physics and Astronomy, University of Manitoba Winnipeg, Manitoba R3T 2N2, Canada and Division of Medical Physics, CancerCare Manitoba, Winnipeg, Manitoba R3E 0V9 and Department of Physics and Astronomy, University of Manitoba Winnipeg, Manitoba R3T 2N2, Canada and Division of Medical Physics, CancerCare Manitoba, Winnipeg, Manitoba R3E 0V9},
abstractNote = {Purpose: In radiation therapy treatment planning, the clinical objectives of uniform high dose to the planning target volume (PTV) and low dose to the organs-at-risk (OARs) are invariably in conflict, often requiring compromises to be made between them when selecting the best treatment plan for a particular patient. In this work, the authors introduce Pareto-Aware Radiotherapy Evolutionary Treatment Optimization (pareto), a multiobjective optimization tool to solve for beam angles and fluence patterns in intensity-modulated radiation therapy (IMRT) treatment planning. Methods: pareto is built around a powerful multiobjective genetic algorithm (GA), which allows us to treat the problem of IMRT treatment plan optimization as a combined monolithic problem, where all beam fluence and angle parameters are treated equally during the optimization. We have employed a simple parameterized beam fluence representation with a realistic dose calculation approach, incorporating patient scatter effects, to demonstrate feasibility of the proposed approach on two phantoms. The first phantom is a simple cylindrical phantom containing a target surrounded by three OARs, while the second phantom is more complex and represents a paraspinal patient. Results: pareto results in a large database of Pareto nondominated solutions that represent the necessary trade-offs between objectives. The solution quality was examined for several PTV and OAR fitness functions. The combination of a conformity-based PTV fitness function and a dose-volume histogram (DVH) or equivalent uniform dose (EUD) -based fitness function for the OAR produced relatively uniform and conformal PTV doses, with well-spaced beams. A penalty function added to the fitness functions eliminates hotspots. Comparison of resulting DVHs to those from treatment plans developed with a single-objective fluence optimizer (from a commercial treatment planning system) showed good correlation. Results also indicated that pareto shows promise in optimizing the number of beams. Conclusions: This initial evaluation of the evolutionary optimization software tool pareto for IMRT treatment planning demonstrates feasibility and provides motivation for continued development. Advantages of this approach over current commercial methods for treatment planning are many, including: (1) fully automated optimization that avoids human controlled iterative optimization and potentially improves overall process efficiency, (2) formulation of the problem as a true multiobjective one, which provides an optimized set of Pareto nondominated solutions refined over hundreds of generations and compiled from thousands of parameter sets explored during the run, and (3) rapid exploration of the final nondominated set accomplished by a graphical interface used to select the best treatment option for the patient.},
doi = {10.1118/1.3615622},
journal = {Medical Physics},
issn = {0094-2405},
number = 9,
volume = 38,
place = {United States},
year = {2011},
month = {9}
}