Improved swarm intelligence solution in large scale radiation therapy inverse planning

Modiri, Arezoo; Gu, Xuejun; Hagan, A; Sawant, Amit

doi:10.1109/glbc.2015.7158300

Cited by 8 publications

(8 citation statements)

References 12 publications

(16 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have observed that the computation time scales inversely linear with the number of allocated nodes. Other optimization algorithms like particle swarm 21 and simulated annealing 22 have the ability to handle nonconvex optimization problems as well. The integration of those methods in a multiobjective bilevel optimization framework introduced here is beyond the scope of this work and might be a subject of future studies.…”

Section: Discussionmentioning

confidence: 99%

A Pareto‐based beam orientation optimization method for spot scanning intensity‐modulated proton therapy

Sayed¹,

Herman²,

Beltran³

2020

Medical Physics

View full text Add to dashboard Cite

Purpose: To provide a proof of principle of a Pareto-based method to automatically generate optimal intensity-modulated proton therapy (IMPT) plans for various noncoplanar beam orientations. Methods: A novel multicriteria beam orientation optimization (MCBOO) method was developed to generate Pareto database of optimal plans. The MCBOO method automatically explores the beam orientations and the scalarization parameters of the IMPT plans simultaneously. The MCBOO method is based on multicriteria bilevel optimization (i.e., hierarchical optimization with two nested levels, named the upper and lower level optimization). In MCBOO, the upper level optimization explores the noncoplanar beam orientation space, while the lower level explores the scalarization parameters for a given beam orientation. Differential evolution method was used in both levels, and the Pareto optimal plans were aggregated from the bilevel optimizations to construct the Pareto database. The MCBOO method was implemented on a multinode multi-GPU cluster, and it was tested on three brain tumor patient cases. The Pareto database of the three patients was generated for a set of DVHbased objectives. A statistical analysis was performed between a selected set of MCBOO plans and the manual plan (plan with manually selected beam orientation based on the clinical experience and optimized with the same single plan iterative optimizer used in the MCBOO). The selected set of MCBOO plans consisted of plans that matched the performance of the manual plan [i.e., MCBOO plans that have the same target coverage (within 2%) as the manual plan or better and achieved the same dose (within 2%) or lower to all of the organs at risks (OARs) but one OAR]. Additionally, a dosimetric comparison between of one of the selected MCBOO plans vs the manual plan was conducted. Results: The multicriteria beam orientation optimization algorithm automatically generated Pareto plans for the three noncoplanar brain tumor cases. The MCBOO plans provided an alternative objective trade-offs to the manual plan. The selected MCBOO plans showed a reduction in dose to multiple organs at risk vs the manual plan with a maximum value which ranged between 10.8 and 12.9 Gy for the three patients. The trade-off of the OAR dose reduction resulted in higher dose to no more than one OAR for each of the selected MCBOO plans vs the manual plan. The maximum dose increase in the MCBOO plans over the manual plan ranged from 7.8 to 11.8 Gy.Conclusions: A novel multicriteria beam orientation optimization method was developed and tested on three IMPT patient cases. The method automatically generates Pareto plans database by exploring the noncoplanar beam orientations. The method was able to identify beam orientations with Pareto optimal plans that are comparable to the manually created plans with varying objective trade-offs.

show abstract

Section: Discussionmentioning

confidence: 99%

A Pareto‐based beam orientation optimization method for spot scanning intensity‐modulated proton therapy

Sayed¹,

Herman²,

Beltran³

2020

Medical Physics

View full text Add to dashboard Cite

show abstract

“…In our preliminary study reported in [29], at each iteration, each infeasible particle position was replaced by a set of scaled positions (on a corresponding line in the simplified model of Fig. 4a), as if the swarm size was multiplied.…”

Section: Optimization Techniquementioning

confidence: 99%

“…A preliminary version of this work was reported in an IEEE conference and AAPM abstracts [29–31]. Although the idea of virtual search was proposed in our preliminary reports, the algorithm was different.…”

Section: Introductionmentioning

confidence: 99%

Radiotherapy Planning Using an Improved Search Strategy in Particle Swarm Optimization

Modiri

Hagan

et al. 2017

IEEE Trans. Biomed. Eng.

Self Cite

View full text Add to dashboard Cite

Objective Evolutionary stochastic global optimization algorithms are widely used in large-scale, non-convex problems. However, enhancing the search efficiency and repeatability of these techniques often requires well-customized approaches. This study investigates one such approach. Methods We use particle swarm optimization (PSO) algorithm to solve a 4-dimensional radiation therapy (RT) inverse planning problem, where the key idea is to use respiratory motion as an additional degree of freedom in lung cancer RT. The primary goal is to administer a lethal dose to the tumor target while sparing surrounding healthy tissue. Our iteratively adjusts radiation fluence-weights for all beam apertures across all respiratory phases. We implement three PSO-based approaches: conventionally-used unconstrained, hard-constrained and our proposed virtual search. As proof of concept, five lung cancer patient cases are optimized over ten runs using each PSO approach. For comparison, a dynamically penalized likelihood (DPL) algorithm- a popular RT optimization technique is also implemented and used. Results The proposed technique significantly improves the robustness to random initialization while requiring fewer iteration cycles to converge across all cases. DPL manages to find the global optimum in 2 out of 5 RT cases over significantly more iterations. Conclusion The proposed virtual search approach boosts the swarm search efficiency and, consequently, improves the optimization convergence rate and robustness for PSO. Significance RT planning is a large-scale, non-convex optimization problem, where finding optimal solutions in a clinically practical time is critical. Our proposed approach can potentially improve the optimization efficiency in similar time-sensitive problems.

show abstract

“…It has been reported that DVH‐based objective functions show nonconvex behavior . MCO based on convex geometrical approximations has been tested clinically and it was reported that the navigated plans show discrepancies from the optimal deliverable plans .…”

Section: Introductionmentioning

confidence: 99%

“…11 It has been reported that DVH-based objective functions show nonconvex behavior. 13,14 MCO based on convex geometrical approximations has been tested clinically and it was reported that the navigated plans show discrepancies from the optimal deliverable plans. 15,16 The reported discrepancy could be attributed to several factors including the use of the WS method, which lacks the ability to find solutions on the nonconvex Pareto front, 12,17 or the error may originate from the convex geometrical approximations.…”

Section: Introductionmentioning

confidence: 99%

Adaptive method for multicriteria optimization of intensity‐modulated proton therapy

Kamal-Sayed

Tseung

et al. 2018

Medical Physics

View full text Add to dashboard Cite

Purpose Provide an adaptive multicriteria optimization (MCO) method for intensity‐modulated proton therapy (IMPT) utilizing GPU technology. Previously described limitations of MCO such as Pareto approximation and limitation on the number of objectives were addressed. Methods The treatment planning process for IMPT must account for multiple objectives, which requires extensive treatment planning resources. Often a large number of objectives (>10) are required. Hence the need for an MCO algorithm that can handle large number of objectives. The novelty of the MCO method presented here lies on the introduction of the adaptive weighting scheme that can generate a well‐distributed and dense representation of the Pareto surface for a large number of objectives in an efficient manner. In our approach the generated Pareto surface is constructed for a set of DVH objectives. The MCO algorithm is based on the augmented weighted Chebychev metric (AWCM) method with an adaptive weighting scheme. This scheme uses the differential evolution (DE) method to generate a set of well‐distributed Pareto points. The quality of the Pareto points’ distribution in the objective space was assessed quantitatively using the Pareto sampling metric. The MCO algorithm was developed to perform multiple parallel searches to achieve a rapid mapping of the Pareto surface, produce clinically deliverable plans, and was implemented on a GPU cluster. The MCO algorithm was tested on two clinical cases with 10 and 18 objectives. For each case one of the MCO‐generated plans was selected for comparison with the clinically generated plan. The MCO plan was randomly selected out of the set of MCO plans that had target coverage similar to the clinically generated plan and the same or better sparing of the organs at risk (OAR). Additionally, a validation study of the AWCM method vs the weighted sum method was performed. Results The adaptive MCO algorithm generated Pareto points on the Pareto hypersurface in a fast (2–3 hr) and efficient manner for 2 cases with 10 and 18 objectives. The MCO algorithm generated a dense and well‐distributed set of Pareto points on the objective space, and was able to achieve minimization of the Pareto sampling metric. The selected MCO plan showed an improvement of the DVH objectives in comparison to the clinically optimized plan in both cases. For case one, the MCO plan showed a 48% reduction of the 50% dose to OARs and a 16% reduction of the 1% dose to OARs. For case 2, the MCO plan showed a 72% reduction of the 50% dose to OARs and a 42% reduction of the 1% dose to OARs. The comparison of AWCM to WS showed that the AWCM method has a dosimetric advantage over WS for both patient cases. Conclusion We introduced an adaptive MCO algorithm for IMPT accelerated using GPUs. The algorithm is based on an adaptive method for generating Pareto plans in the objective space. We have shown that the algorithm can provide rapid and efficient mapping of the multicriteria Pareto surface with clinically deliverable plans.

show abstract

Improved swarm intelligence solution in large scale radiation therapy inverse planning

Cited by 8 publications

References 12 publications

A Pareto‐based beam orientation optimization method for spot scanning intensity‐modulated proton therapy

A Pareto‐based beam orientation optimization method for spot scanning intensity‐modulated proton therapy

Radiotherapy Planning Using an Improved Search Strategy in Particle Swarm Optimization

Adaptive method for multicriteria optimization of intensity‐modulated proton therapy

Contact Info

Product

Resources

About