A GPU-based multi-criteria optimization algorithm for HDR brachytherapy

Bélanger, Cédric; Cui, Songye; Ma, Y.; Després, Philippe; Cunha, J. Adam M.; Beaulieu, Luc

doi:10.1088/1361-6560/ab1817

Cited by 27 publications

(58 citation statements)

References 30 publications

(65 reference statements)

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“…As mentioned above the parallel processing reduces the computation time drastically, which is deemed necessary to implement MCBOO in the clinical practice. In our implementation, a large number of GPUs were required in comparison to other GPU‐based MCO methods . This is due to the large number of optimization variables, large number of objectives, and the need for a fine dose calculation grid for IMPT plans .…”

Section: Discussionmentioning

confidence: 99%

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

Sayed¹,

Herman²,

Beltran³

2020

Medical Physics

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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.

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Section: Discussionmentioning

confidence: 99%

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

Sayed¹,

Herman²,

Beltran³

2020

Medical Physics

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“…Thus, in this work, four seed positions in a single catheter were used to deliver a 20 Gy single dose to the planning tumor volume (PTV). On the other hand, automatic brachytherapy planning can be optimized using multicriteria optimization algorithms that demand high-speed computing capabilities, see Belanger et al [30]. However, it is still a regular practice in brachytherapy planning to manually fine-tune the seed positions and dwells times to obtain a case-specific valid plan [31].…”

Section: Plos Onementioning

confidence: 99%

Monte Carlo simulation of the effect of magnetic fields on brachytherapy dose distributions in lung tissue material

et al. 2020

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The aim of this work was to use TOPAS Monte Carlo simulations to model the effect of magnetic fields on dose distributions in brachytherapy lung treatments, under ideal and clinical conditions. Idealistic studies were modeled consisting of either a monoenergetic electron source of 432 keV, or a polyenergetic electron source using the spectrum of secondary electrons produced by 192 Ir gamma-ray irradiation. The electron source was positioned in the center of a homogeneous, lung tissue phantom (ρ = 0.26 g/cm 3). Conversely, the clinical study was simulated using the VariSource VS2000 192 Ir source in a patient with a lung tumor. Three contoured volumes were considered: the tumor, the planning tumor volume (PTV), and the lung. In all studies, dose distributions were calculated in the presence or absence of a constant magnetic field of 3T. Also, TG-43 parameters were calculated for the VariSource and compared with published data from EGS-brachy (EGSnrc) and PENEL-OPE. The magnetic field affected the dose distributions in the idealistic studies. For the monoenergetic and poly-energetic studies, the radial distance of the 10% iso-dose line was reduced in the presence of the magnetic field by 64.9% and 24.6%, respectively. For the clinical study, the magnetic field caused differences of 10% on average in the patient dose distributions. Nevertheless, differences in dose-volume histograms were below 2%. Finally, for TG-43 parameters, the dose-rate constant from TOPAS differed by 0.09% ± 0.33% and 0.18% ± 0.33% with respect to EGS-brachy and PENELOPE, respectively. The geometry and anisotropy functions differed within 1.2% ± 1.1%, and within 0.0% ± 0.3%, respectively. The Lorentz forces inside a 3T magnetic resonance machine during 192 Ir brachytherapy treatment of the lung are not large enough to affect the tumor dose distributions significantly, as expected. Nevertheless, large local differences were found in the lung tissue. Applications of this effect are therefore limited by the fact that meaningful differences appeared only in regions containing air, which is not abundant inside the human.

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“…In practice, BT planners need to run available software many times with different penalty factors (i.e., effectively creating new optimization models) or need to perform graphical optimization (i.e., directly modifying the spatial radiation dose distribution, and thereby adjusting dwell times) until satisfactory plans are obtained [4]. A few researches [9][10][11] have worked toward the automation of solving these linear models or quadratic models with multiple optimization runs, in which each run has a different setting of penalty factors, resulting in multiple treatment plans (300 in [9,10] and 1000 in [11]). The obtained plans are then filtered to identify the ones that satisfy all the DV criteria in the clinical protocol.…”

Section: Computational Challenges In Hdr-bt Planning and Related Workmentioning

confidence: 99%

“…The more DV criteria are considered, the higher the probability that such a situation might happen. For example, the protocol considered in [9][10][11] has only four DV criteria while the one at our clinic has nine DV criteria (see Table 1). Different rounds of multiple optimization runs then need to be performed again until satisfactory plans are obtained.…”

Section: Computational Challenges In Hdr-bt Planning and Related Workmentioning

confidence: 99%

Fast and insightful bi-objective optimization for prostate cancer treatment planning with high-dose-rate brachytherapy

Luong

Alderliesten

Pieters

et al. 2019

Applied Soft Computing

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h i g h l i g h t s• Challenges of the brachytherapy planning problem for prostate cancer treatment.• Accuracy and efficiency issues in multi-objective prostate brachytherapy planning. • A multi-resolution scheme to accelerate the optimization of brachytherapy planning. • A multi-core CPU parallelization of our approach in brachytherapy planning. • Comparing solutions created by our approach with clinically approved treatment plans. a b s t r a c tPurpose: Prostate high-dose-rate brachytherapy (HDR-BT) planning involves determining the movement that a high-strength radiation stepping source travels through the patient's body, such that the resulting radiation dose distribution sufficiently covers tumor volumes and safely spares nearby healthy organs from radiation risks. The Multi-Objective Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (MO-RV-GOMEA) has been shown to be able to effectively handle this inherent bi-objective nature of HDR-BT planning. However, in clinical practice there is a very restricted planning time budget (often less than 1 h) for HDR-BT planning, and a considerable amount of running time needs to be spent before MO-RV-GOMEA finds a good trade-off front of treatment plans (about 20-30 min on a single CPU core) with sufficiently accurate dose calculations, limiting the applicability of the approach in the clinic. To address this limitation, we propose an efficiency enhancement technique for MO-RV-GOMEA solving the bi-objective prostate HDR-BT planning problem. Methods: Dose-Volume (DV) indices are often used to assess the quality of HDR-BT plans. The accuracy of these indices depends on the number of dose calculation points at which radiation doses are computed. These are randomly uniformly sampled inside target volumes and organs at risk. In available HDR-BT planning optimization algorithms, the number of dose calculation points is fixed. The more points are used, the better the accuracy of the obtained results will be, but also the longer the algorithms need to be run. In this work, we introduce a so-called multi-resolution scheme that gradually increases the number of dose calculation points during the optimization run such that the running time can be substantially reduced without compromising on the accuracy of the obtained results. Results and conclusion: Experiments on a data set of 18 patient cases show that with the multiresolution scheme, MO-RV-GOMEA can achieve a sufficiently good trade-off front of treatment plans after five minutes of running time on a single CPU core (4-6 times faster than the old approach with a fixed number of dose calculation points). When the optimization with the multi-resolution scheme is run on a quad-core machine, five minutes are enough to obtain trade-off fronts that are nearly as good as those obtained by running optimization with the old approach in one hour (i.e., 12 times faster). This leaves ample time to perform the selection of the preferred treatment plan from the trade-off front for the specific patient at hand. Furthermore, compa...

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A GPU-based multi-criteria optimization algorithm for HDR brachytherapy

Cited by 27 publications

References 30 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

Monte Carlo simulation of the effect of magnetic fields on brachytherapy dose distributions in lung tissue material

Fast and insightful bi-objective optimization for prostate cancer treatment planning with high-dose-rate brachytherapy

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