Noncoplanar beam angle optimization in IMRT treatment planning using pattern search methods

Rocha, Humberto; Dias, Joana; Ferreira, B.; Lopes, Mário

doi:10.1088/1742-6596/616/1/012014

Cited by 8 publications

(5 citation statements)

References 38 publications

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“…We have developed deterministic direct-search approaches for BAO that were able to obtain high-quality treatment plans [5][6][7][8][9][10][11]. Although these approaches imply a computational time that is compatible with the clinical practice, these computational times are significant and can represent a drawback in some practical situations.…”

Section: Randomized Direct-search For Baomentioning

confidence: 99%

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A Randomized Direct-Search Approach for Beam Angle Optimization in Intensity-Modulated Proton Therapy

Rocha

Dias

2021

Computational Science and Its Applications – ICCSA 2021

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Intensity-modulated proton therapy (IMPT) is a very promising alternative for radiotherapy due to the unique depth-dose characteristics of protons that allow better trade-offs between tumor irradiation and organ sparing. Optimal selection of proton beam directions -beam angle optimization (BAO) -plays a decisive role in further improving these trade-offs having a profound impact on the quality of dose distributions, particularly because in IMPT the number of beams is typically lower than in intensity-modulated radiation therapy for photons (IMRT). Computational time efficiency becomes even more critical in the optimization of proton beam directions due to the increased degrees of freedom provided by different levels of energy and the existence of different scenarios for robust IMPT plans. In this study, we consider direct-search methods to address the IMPT BAO problem given their good performance in the resolution of the IMRT BAO problem. In order to test the effectiveness of reducing the number of polling directions at each iteration, both in terms of computational time and quality of the solution, a strategy for randomly selecting a reduced number of polling directions among a set of evenly distributed directions across quadrants is proposed. This strategy considers a set of probabilistic directions, where a descent direction exists with a given probability, instead of deterministic directions that guarantee at least one descent direction. For the prostate cancer case used in the computational tests, the randomized strategy proposed shows that considering as few as two polling directions improved significantly the computational time while the resulting treatment plan is at least as good as that obtained by the deterministic method. In future work, this type of randomized approximation has to be extended and tested in different cancer cases to validate the excellent performance found for a single prostate cancer case.

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Section: Randomized Direct-search For Baomentioning

confidence: 99%

“…In previous works, direct-search methods proved to be suited for BAO in IMRT [5][6][7][8][9][10][11]. Although direct-search approaches require few function evaluations to converge, several attempts were made to further improve its computational time performance, including considering FMO surrogates [5] or reducing the search space [9].…”

Section: Introductionmentioning

confidence: 99%

A Randomized Direct-Search Approach for Beam Angle Optimization in Intensity-Modulated Proton Therapy

Rocha

Dias

2021

Computational Science and Its Applications – ICCSA 2021

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“…An alternative formulation of the BAO problem has been considered in our works. All possible continuous beam angle directions around the tumor have been considered instead of a discretized set of beam directions, leading to a continuous global optimization problem [24,25,26,27,28]. The continuous formulation of the noncoplanar BAO problem is briefly described next.…”

Section: Noncoplanar Beam Angle Optimizationmentioning

confidence: 99%

An Optimization Approach for Noncoplanar Intensity-Modulated Arc Therapy Trajectories

Rocha

Dias

Ventura

et al. 2019

Computational Science and Its Applications – ICCSA 2019

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The latest generation of linear accelerators allows the simultaneous motion of gantry and couch leading to highly noncoplanar arc trajectories. The use of noncoplanar trajectories in arc radiotherapy was recently proposed to combine the benefits of arc treatment plans, such as short treatment times, with the benefits of step-and-shoot noncoplanar intensity-modulated radiation therapy (IMRT) treatment plans, such as improved organ sparing. In this paper, a two-step approach for the optimization of highly noncoplanar arc trajectories is presented and tested using a complex nasopharyngeal tumor case already treated at the Portuguese Institute of Oncology of Coimbra. In the first step, a set of noncoplanar beam directions is calculated resorting to one of the beam angle optimization (BAO) algorithms proposed in our previous works for step-and-shoot IMRT. In the second step, anchored in the points (beam directions) calculated in the first step, the proposed optimization strategy determines iteratively more anchor points that will define the noncoplanar arc trajectory, considering the dosimetric criteria used for the noncoplanar BAO search rather than geometric or time criteria commonly used. For the patient tested, the resulting noncoplanar arc therapy plan has undoubtedly greater overall quality compared to both the coplanar arc therapy plan and the typically used coplanar equispaced step-and-shoot IMRT plan.

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“…Another problem in some forms of radiation therapy is the placement of the ionising beam directions [4,15,21,51]. This is a combinatorial problem, often solved through heuristics, requiring solving hundreds or thousands smaller and larger optimisation problems before finding an acceptable solution.…”

Section: Introductionmentioning

confidence: 99%

An interior-point implementation developed and tuned for radiation therapy treatment planning

2017

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While interior-point methods share the same fundamentals, the implementation determines the actual performance. In order to attain the highest efficiency, different applications may require differently tuned implementations. In this paper we describe an implementation specifically designed for optimisation in radiation therapy. These problems are large-scale nonlinear (and sometimes nonconvex) constrained optimisation problems, consisting of both sparse and dense data. Several applicationspecific properties are exploited to enhance efficiency. Permuting, tiling and mixed precision arithmetic allow the algorithm to optimally process the mixed dense and sparse data matrices (making this step 2.2 times faster, and overall runtime reduction of 55%) and scalability (16 threads resulted in a speed-up factor of 9.8 compared to singlethreaded performance, against a speed-up factor of 7.7 for the less optimised implementation). Predefined cost-functions are hard-coded and the computationally expensive second derivatives are written in canonical form, and combined if multiple cost-functions are defined for the same clinical structure. The derivatives are then computed using a scaled matrix-matrix product. A cheap initialisation strategy based on the background knowledge reduces the number of iterations by 11%. We also propose a novel combined Mehrotra-Gondzio approach. The algorithm is extensively tested on a dataset consisting of 120 patients, distributed over 6 tumour sites/approaches. This test dataset is made publicly available.

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Noncoplanar beam angle optimization in IMRT treatment planning using pattern search methods

Cited by 8 publications

References 38 publications

A Randomized Direct-Search Approach for Beam Angle Optimization in Intensity-Modulated Proton Therapy

A Randomized Direct-Search Approach for Beam Angle Optimization in Intensity-Modulated Proton Therapy

An Optimization Approach for Noncoplanar Intensity-Modulated Arc Therapy Trajectories

An interior-point implementation developed and tuned for radiation therapy treatment planning

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