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David Craft - One of the best experts on this subject based on the ideXlab platform.
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effective heuristics for Beam Angle optimization in radiation therapy
Simulation, 2018Co-Authors: Hamed Yarmand, David CraftAbstract:In radiation therapy, the main challenge is to deliver the dose to the tumor while sparing healthy tissues around the tumor. One important decision to make is the Beam configuration. The corresponding mathematical problem, known as Beam Angle optimization (BAO), is a large-scale problem. We propose three novel heuristic approaches to reduce the computation time and find high-quality treatment plans for BAO. The first heuristic is based on the fact that the Beams that are geometrically close to each other (i.e., ‘adjacent’ Beams) have similar impacts, and hence are less likely to be used in the optimal configuration simultaneously. Therefore, in this heuristic, referred to as ‘neighbor cuts’, their use is limited. The second heuristic is to eliminate the Beams with small contribution to dose delivery in the ideal plan when all candidate Beams can be used. Finally, the number of Beams is reduced in the third heuristic while ensuring the quality of the plan remains within a pre-specified range. These heurist...
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A hybrid approach to Beam Angle optimization in intensity-modulated radiation therapy
Computers & Operations Research, 2013Co-Authors: Dimitris Bertsimas, David Craft, Valentina Cacchiani, Omid NohadaniAbstract:Intensity-Modulated Radiation Therapy is the technique of delivering radiation to cancer patients by using non-uniform radiation fields from selected Angles, with the aim of reducing the intensity of the Beams that go through critical structures while reaching the dose prescription in the target volume. Two decisions are of fundamental importance: to select the Beam Angles and to compute the intensity of the Beams used to deliver the radiation to the patient. Often, these two decisions are made separately: first, the treatment planners, on the basis of experience and intuition, decide the orientation of the Beams and then the intensities of the Beams are optimized by using an automated software tool. Automatic Beam Angle selection (also known as Beam Angle Optimization) is an important problem and is today often based on human experience. In this context, we face the problem of optimizing both the decisions, developing an algorithm which automatically selects the Beam Angles and computes the Beam intensities. We propose a hybrid heuristic method, which combines a simulated annealing procedure with the knowledge of the gradient. Gradient information is used to quickly find a local minimum, while simulated annealing allows to search for global minima. As an integral part of this procedure, the Beam intensities are optimized by solving a Linear Programming model. The proposed method presents a main difference from previous works: it does not require to have on input a set of candidate Beam Angles. Indeed, it dynamically explores Angles and the only discretization that is necessary is due to the maximum accuracy that can be achieved by the linear accelerator machine. Experimental results are performed on phantom and real-life case studies, showing the advantages that come from our approach.
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mo a 137 03 effective heuristic cuts for Beam Angle optimization in radiation therapy
Medical Physics, 2013Co-Authors: Hamed Yarmand, David CraftAbstract:Purpose: To improve the solution efficiency (i.e., reducing computation time while obtaining high‐quality treatment plans) for the Beam Angle optimization problem (BAO) in radiation therapy treatment planning. Methods: We formulate BAO as a mixed integer programming problem (MIP) whose solution gives the optimal Beam orientation as well as optimal Beam intensity map. We then incorporate a family of heuristic cuts into the resultant MIP based on the observation that the number of candidate Beams is usually much larger than the number of Beams used in the treatment plan, and therefore, it is less likely that adjacent Beams are simultaneously used in the optimal treatment plan. The proposed cuts, referred to as ((neighbor cuts)), force the optimization system to choose one or a few Beams from any set of adjacent Beams. As a Result, the search space and the computation time are reduced considerably. The neighbor cuts can be added to any MIP formulation for BAO including the cases of coplanar/noncoplanar Beams for intensity modulated radiation therapy (IMRT) and stereotactic body radiation therapy (SBRT). For the numerical experiments a liver case (both IMRT and SBRT) with 34 and a head and neck case (only IMRT) with 36 coplanar Beams were considered. Each Beam was divided into 109‐144 Beamlets of size 1*1 cm2. Results: We first solved the corresponding MIP without the neighbor cuts and recorded the optimal solution and the computation time. Then we incorporated the neighbor cuts into the MIP and resolved it. Our results show that incorporating the neighbor cuts into the MIP reduces the computation time considerably and results in solutions with negligible optimality gaps. Conclusion: This research incorporates the observation that optimal Beam configurations are typically a sparse set of well‐spaced Beams to improve the efficiency of the solution technique drastically. The project was supported by the Federal Share of program income earned by Massachusetts General Hospital on C06 CA059267, Proton Therapy Research and Treatment Center and also partially by RaySearch laboratories.
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simultaneous navigation of multiple pareto surfaces with an application to multicriteria imrt planning with multiple Beam Angle configurations
Medical Physics, 2010Co-Authors: David Craft, Michael MonzAbstract:Purpose: To introduce a method to simultaneously explore a collection of Pareto surfaces. The method will allow radiotherapy treatment planners to interactively explore treatment plans for different Beam Angle configurations as well as different treatment modalities. Methods: The authors assume a convex optimization setting and represent the Pareto surface for each modality or given Beam set by a set of discrete points on the surface. Weighted averages of these discrete points produce a continuous representation of each Pareto surface. The authors calculate a set of Pareto surfaces and use linear programming to navigate across the individual surfaces, allowing switches between surfaces. The switches are organized such that the plan profits in the requested way, while trying to keep the change in dose as small as possible. Results: The system is demonstrated on a phantom pancreas IMRT case using 100 different five Beam configurations and a multicriteria formulation with six objectives. The system has intuitive behavior and is easy to control. Also, because the underlying linear programs are small, the system is fast enough to offer real-time exploration for the Pareto surfaces of the given Beam configurations. Conclusions: The system presented offers a sound starting point for building clinical systems for multicriteria exploration of different modalities and offers a controllable way to explore hundreds of Beam Angle configurations in IMRT planning, allowing the users to focus their attention on the dose distribution and treatment planning objectives instead of spending excessive time on the technicalities of delivery.
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local Beam Angle optimization with linear programming and gradient search
Physics in Medicine and Biology, 2007Co-Authors: David CraftAbstract:The optimization of Beam Angles in IMRT planning is still an open problem, with literature focusing on heuristic strategies and exhaustive searches on discrete Angle grids. We show how a Beam Angle set can be locally refined in a continuous manner using gradient-based optimization in the Beam Angle space. The gradient is derived using linear programming duality theory. Applying this local search to 100 random initial Angle sets of a phantom pancreatic case demonstrates the method, and highlights the many-local-minima aspect of the BAO problem. Due to this function structure, we recommend a search strategy of a thorough global search followed by local refinement at promising Beam Angle sets. Extensions to nonlinear IMRT formulations are discussed.
Radhe Mohan - One of the best experts on this subject based on the ideXlab platform.
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iterative solution methods for Beam Angle and fluence map optimization in intensity modulated radiation therapy planning
OR Spectrum, 2008Co-Authors: Jaewon Choi, Radhe MohanAbstract:We present computational approaches for optimizing Beam Angles and fluence maps in Intensity Modulated Radiation Therapy (IMRT) planning. We assume that the number of Angles to be used for the treatment is given by the treatment planner. A mixed integer programming (MIP) model and a linear programming (LP) model are used to find an optimal set of Beam Angles and their corresponding fluence maps. The MIP model is solved using the branch-and-bound method while the LP model is solved using the interior point method. In order to reduce the computational burden for solving the optimization models, we introduce iterative Beam Angle elimination algorithms in which an insignificant Beam Angle is eliminated in each iteration. Other techniques are also explored including feasible set reduction for LP and data reduction. Experiments are made to show the computational advantage of the iterative methods for optimizing Angles using real patient cases.
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Iterative solution methods for Beam Angle and fluence map optimization in intensity modulated radiation therapy planning
OR Spectrum, 2007Co-Authors: Gino J. Lim, Jaewon Choi, Radhe MohanAbstract:We present computational approaches for optimizing Beam Angles and fluence maps in Intensity Modulated Radiation Therapy (IMRT) planning. We assume that the number of Angles to be used for the treatment is given by the treatment planner. A mixed integer programming (MIP) model and a linear programming (LP) model are used to find an optimal set of Beam Angles and their corresponding fluence maps. The MIP model is solved using the branch-and-bound method while the LP model is solved using the interior point method. In order to reduce the computational burden for solving the optimization models, we introduce iterative Beam Angle elimination algorithms in which an insignificant Beam Angle is eliminated in each iteration. Other techniques are also explored including feasible set reduction for LP and data reduction. Experiments are made to show the computational advantage of the iterative methods for optimizing Angles using real patient cases.
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Beam Angle optimization and reduction for intensity-modulated radiation therapy of non–small-cell lung cancers
International Journal of Radiation Oncology Biology Physics, 2006Co-Authors: H. Helen Liu, Xiaochun Wang, Lei Dong, Xiaodong Zhang, Maria Jauregui, Radhe MohanAbstract:Purpose: To optimize Beam Angles and reduce the number of Beams used for intensity-modulated radiation therapy (IMRT) of non–small-cell lung cancer (NSCLC). Methods and Materials: An exhaustive search scheme was used to perform Beam Angle optimization (BAO) for IMRT of NSCLC. This approach involved intercomparison of all possible Beam Angle combinations and selection of the best Angles based on the scores or costs of the objective functions used in the treatment plan optimization. Ten Stage III NSCLC cases were selected to evaluate the BAO algorithm and dosimetry benefits of IMRT-BAO. IMRT plans using five or seven coplanar Beams were optimized and compared with those using nine equal-spaced Beams. Results of BAO were also compared between plans using different numbers of Beams with or without fluence modulation. Results: Each anatomic structure, e.g., tumor or lung, had its own preferred Beam Angles. Thus, BAO required appropriate balance of competing objective functions. Plans using fewer Angles (five or seven Beams) could achieve plan quality similar to those using nine equal-spaced Beams, however with reduced monitor units and field segments. The number of Beams used for the treatment (five vs. seven) and the fluence modulation (open or IMRT Beams) did not have a significant impact on the results of the BAO. Conclusions: Use of fewer Beams (e.g., five) for lung IMRT could result in acceptable plan quality but improved treatment efficiency. A multiresolution search scheme could be developed for BAO using fewer and nonmodulated Beams to reduce the computation cost of BAO.
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Effectiveness of noncoplanar IMRT planning using a parallelized multiresolution Beam Angle optimization method for paranasal sinus carcinoma
International Journal of Radiation Oncology Biology Physics, 2005Co-Authors: Xiaochun Wang, Michael Gillin, Anesa Ahamad, Lei Dong, Xiaodong Zhang, Radhe MohanAbstract:Purpose: To determine the effectiveness of noncoplanar Beam configurations and the benefit of plans using fewer but optimally placed Beams designed by a parallelized multiple-resolution Beam Angle optimization (PMBAO) approach. Methods and Materials: The PMBAO approach uses a combination of coplanar and noncoplanar Beam configurations for intensity-modulated radiation therapy (IMRT) treatment planning of paranasal sinus cancers. A smaller number of Beams (e.g. 3) are first used to explore the solution space to determine the best and worst Beam directions. The results of this exploration are then used as a starting point for determining an optimum Beam orientation configuration with more Beams (e.g. 5). This process is parallelized using a message passing interface, which greatly reduces the overall computation time for routine clinical practice. To test this approach, treatment for 10 patients with paranasal sinus cancer was planned using a total of 5 Beams from a pool of 46 possible Beam Angles. The PMBAO treatment plans were also compared with IMRT plans designed using 9 equally spaced coplanar Beams, which is the standard approach in our clinic. Plans with these two different Beam configurations were compared with respect to dose conformity, dose heterogeneity, dose–volume histograms, and doses to organs at risk (i.e., eyes, optic nerve, optic chiasm, and brain). Results: The noncoplanar Beam configuration was superior in most paranasal sinus carcinoma cases. The target dose homogeneity was better using a PMBAO 5-Beam configuration. However, the dose conformity using PMBAO was not improved and was case dependent. Compared with the 9-Beam configuration, the PMBAO configuration significantly reduced the mean dose to the eyes and optic nerves and the maximum dose to the contralateral optical path (e.g. the contralateral eye and optic nerve). The maximum dose to the ipsilateral eye and optic nerve was also lower using the PMBAO configuration than using the 9-Beam configuration, although this difference was not significant. The mean doses to the optic chiasm and brain are marginally lower using the PMBAO configuration than using 9-Beam configuration. The maximum doses to the optic chiasm and brain are the same with the PMBAO configuration and the 9-Beam configuration. Conclusion: Parallelized multiple-resolution Beam Angle optimization with an optimized noncoplanar Beam configuration is an effective and practical approach for IMRT treatment planning. Five-Beam treatment plans optimized using the PMBAO are at least equivalent to, and overall better than, the plans using 9 equally spaced coplanar Beams.
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development of methods for Beam Angle optimization for imrt using an accelerated exhaustive search strategy
International Journal of Radiation Oncology Biology Physics, 2004Co-Authors: Xiaochun Wang, Lei Dong, Xiaodong Zhang, Helen Liu, Radhe MohanAbstract:Purpose: The purpose of this article is to explore the use of the accelerated exhaustive search strategy for developing and validating methods for optimizing Beam orientations for intensity-modulated radiation therapy (IMRT). Combining Beam-Angle optimization (BAO) with intensity distribution optimization is expected to improve the quality of IMRT treatment plans. However, BAO is one of the most difficult problems to solve adequately because of the huge hyperspace of possible Beam configurations (e.g., selecting 7 of 36 uniformly spaced coplanar Beams would require the intercomparison of 8,347,680 IMRT plans). Methods and Materials: An “influence vector” (IV) approximation technique for high-speed estimation of IMRT dose distributions was used in combination with a fast gradient search algorithm (Newton’s method) for IMRT optimization. In the IV approximation, it is assumed that the change in intensity of a ray (or bixel) proportionately changes dose along the ray. Evidence is presented that the IV approximation is valid for BAO. The scatter contribution at points away from the ray is accounted for fully in IMRT optimization after the optimum Beam orientation has been determined. IVs for all candidate Beam Angles are generated before the start of optimization. For all subsets of Beams selected from a given pool of Beams (e.g., 5 of 24 uniformly spaced Beams), the distribution of planning scores for the best and the worst plans, optimum Angle distributions, dose distributions, and dose‐volume histograms (DVH) were analyzed for one prostate and two lung cancer cases. The results of the exhaustive search technique were used to develop a “multiresolution” search strategy. In this approach, a smaller number of Beams (e.g., three) is first used to explore the hyperspace of solutions to determine the most preferred and the least preferred directions. The results of such exploration are then used as a starting point for determining an optimum configuration comprising a larger number of Beams (e.g., seven). This two-step process is considerably faster than full exhaustive search. The question to be answered was whether the two methods lead to the same or similar solutions. The results of exhaustive search and multiresolution approaches were also compared with a previously published approach that used Beam’s-eye-view dosimetrics (BEVD). Results: The relative ranks of plans optimized by an accurate dose calculation method were highly correlated with those of the plans optimized by the fast calculation method (i.e., using the IV approximation), which suggests that an approximate dose calculation algorithm can be used effectively for ranking of plans during BAO. We found that dose distributions and DVH of many Beam configurations within a specified subset from a given pool of Beams (e.g., 5 of 18) may be clinically indistinguishable and acceptable. Their optimized IMRT scores fall in a narrow range, although Beam configurations and dose distributions may be different. We used the frequency distributions as a function of Beam Angles for the best 100 and the worst 100 plans to determine the most and the least preferred Beam Angles. We found that the most and the least preferred Angle distributions for 3 of 18 configurations were very similar to those for 5, 6, 7, or 8 of 18 or 24 configurations, but the size of the search space was much smaller for the 3 of 18 case. Using fewer than three Beams was discovered to be inadequate. This information was used to select the most preferred Angles and eliminate the least preferred ones before searching for the optimum Angles for the remaining Beams. For the cases we studies, the multiresolution strategy produced very similar results to the full exhaustive search. Based on the observation that the worst plans had at least one parallel-opposed pair of Beams and virtually all of the best plans had none, we were able to further reduce the size of the search space dramatically by using a pool of only nonparallel-opposed equispaced Beams (i.e., 7 of 19 instead of 7 of 36). Another observation was that the probability of finding an optimum configuration in a smaller Beam pool is substantially lower than in a larger pool (e.g., 5 of 18 vs. 5 of 24). The implication of this BAO is not very important when a large number of Beams (nine or more) is used and vice versa. Our results showed that the plans with fewer but optimally placed Beams could be as good as or better than plans using a larger number of unoptimized or uniformly placed Beams.
Ben J.m. Heijmen - One of the best experts on this subject based on the ideXlab platform.
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Noncoplanar Beam Angle Class Solutions to Replace Time-Consuming Patient-Specific Beam Angle Optimization in Robotic Prostate Stereotactic Body Radiation Therapy.
International Journal of Radiation Oncology Biology Physics, 2015Co-Authors: L. Rossi, Sebastiaan Breedveld, Shafak Aluwini, Ben J.m. HeijmenAbstract:Purpose To investigate development of a recipe for the creation of a Beam Angle class solution (CS) for noncoplanar prostate stereotactic body radiation therapy to replace time-consuming individualized Beam Angle selection (iBAS) without significant loss in plan quality, using the in-house "Erasmus-iCycle" optimizer for fully automated Beam profile optimization and iBAS. Methods and Materials For 30 patients, Erasmus-iCycle was first used to generate 15-, 20-, and 25-Beam iBAS plans for a CyberKnife equipped with a multileaf collimator. With these plans, 6 recipes for creation of Beam Angle CSs were investigated. Plans of 10 patients were used to create CSs based on the recipes, and the other 20 to independently test them. For these tests, Erasmus-iCycle was also used to generate intensity modulated radiation therapy plans for the fixed CS Beam setups. Results Of the tested recipes for CS creation, only 1 resulted in 15-, 20-, and 25-Beam noncoplanar CSs without plan deterioration compared with iBAS. For the patient group, mean differences in rectum D 1cc , V 60GyEq , V 40GyEq , and D mean between 25-Beam CS plans and 25-Beam plans generated with iBAS were 0.2 ± 0.4 Gy, 0.1% ± 0.2%, 0.2% ± 0.3%, and 0.1 ± 0.2 Gy, respectively. Differences between 15- and 20-Beam CS and iBAS plans were also negligible. Plan quality for CS plans relative to iBAS plans was also preserved when narrower planning target volume margins were arranged and when planning target volume dose inhomogeneity was decreased. Using a CS instead of iBAS reduced the computation time by a factor of 14 to 25, mainly depending on Beam number, without loss in plan quality. Conclusions A recipe for creation of robust Beam Angle CSs for robotic prostate stereotactic body radiation therapy has been developed. Compared with iBAS, computation times decreased by a factor 14 to 25. The use of a CS may avoid long planning times without losses in plan quality.
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icycle integrated multicriterial Beam Angle and profile optimization for generation of coplanar and noncoplanar imrt plans
Medical Physics, 2012Co-Authors: S Breedveld, Pascal R.m. Storchi, P Voet, Ben J.m. HeijmenAbstract:Purpose: To introduce iCycle, a novel algorithm for integrated, multicriterial optimization of Beam Angles, and intensity modulated radiotherapy (IMRT) profiles. Methods: A multicriterial plan optimization with iCycle is based on a prescription calledwish-list, containing hard constraints and objectives with ascribed priorities. Priorities are ordinal parameters used for relative importance ranking of the objectives. The higher an objective priority is, the higher the probability that the corresponding objective will be met. Beam directions are selected from an input set of candidate directions. Input sets can be restricted, e.g., to allow only generation of coplanar plans, or to avoid collisions between patient/couch and the gantry in a noncoplanar setup. Obtaining clinically feasible calculation times was an important design criterium for development of iCycle. This could be realized by sequentially adding Beams to the treatment plan in an iterative procedure. Each iteration loop starts with selection of the optimal direction to be added. Then, a Pareto-optimal IMRT plan is generated for the (fixed) Beam setup that includes all so far selected directions, using a previously published algorithm for multicriterial optimization of fluence profiles for a fixed Beam arrangement Breedveld et al. [Phys. Med. Biol. 54, 7199–7209 (2009)]. To select the next direction, each not yet selected candidate direction is temporarily added to the plan and an optimization problem, derived from the Lagrangian obtained from the just performed optimization for establishing the Pareto-optimal plan, is solved. For each patient, a single one-Beam, two-Beam, three-Beam, etc. Pareto-optimal plan is generated until addition of Beams does no longer result in significant plan quality improvement. Plan generation with iCycle is fully automated. Results: Performance and characteristics of iCycle are demonstrated by generating plans for a maxillary sinus case, a cervical cancer patient, and a liver patient treated with SBRT. Plans generated with Beam Angle optimization did better meet the clinical goals than equiangular or manually selected configurations. For the maxillary sinus and liver cases, significant improvements for noncoplanar setups were seen. The cervix case showed that also in IMRT with coplanar setups, Beam Angle optimization with iCycle may improve plan quality. Computation times for coplanar plans were around 1–2 h and for noncoplanar plans 4–7 h, depending on the number of Beams and the complexity of the site. Conclusions: Integrated Beam Angle and profile optimization with iCycle may result in significant improvements in treatment plan quality. Due to automation, the plan generation workload is minimal. Clinical application has started.
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Multi-criteria Beam Angle IMRT optimization with iCycle
2009Co-Authors: Sebastiaan Breedveld, Pascal R.m. Storchi, Ben J.m. HeijmenAbstract:An essential ingredient for making a good treatment plan for radiation therapy treatment is the selection of suitable Beam Angles. A sub-optimal Beam Angle configuration limits the possibilities of optimization. However, just as with normal IMRT optimization, the trade-offs between the PTV and OARs are not clear a priori. The introduction of multi-criteria optimization offered an intuitive planning approach.
Maria Do Carmo Lopes - One of the best experts on this subject based on the ideXlab platform.
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Advantage of Beam Angle Optimization in Head-and-Neck IMRT: Patient Specific Analysis
IFMBE Proceedings, 2019Co-Authors: Tiago Ventura, Humberto Rocha, Maria Do Carmo Lopes, Brigida C. Ferreira, Joana DiasAbstract:Radiation therapy (RT) main purpose is to eliminate, in a controlled way, all tumor cells sparing as much as possible the normal tissues. Intensity-Modulated Radiation Therapy (IMRT) is becoming the standard treatment technique in RT. Beam Angle optimization (BAO) has potential to confer more quality to IMRT inverse planning process compared to manual trial and error approaches. In this study, the BAO advantages in head-and-neck patients are highlighted, using a patient specific analysis. Fluence optimization was done with Erasmus-iCycle multicriterial engine and BAO optimization was performed using two different algorithms: a combinatorial iterative algorithm and an algorithm based on a pattern search method. Plan assessment and comparison was performed with the graphical tool SPIDERplan. Among a set of forty studied nasopharynx cancer cases, three patients have been select for the specific analysis presented in this work. BAO presented plan quality improvements when Beam angular optimized plans were compared with the equidistant Beam Angle solution and when plans based on non-coplanar Beams geometries were compared with coplanar arrangements. Improvement in plan quality with a reduced number of Beams was also achieved, in one case. For all cases, BAO generated plans with higher target coverage and better sparing of the normal tissues.
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comparison of combinatorial and continuous frameworks for the Beam Angle optimization problem in imrt
International Conference on Computational Science and Its Applications, 2018Co-Authors: Humberto Rocha, Tiago Ventura, Joana Dias, Brigida C. Ferreira, Maria Do Carmo LopesAbstract:Radiation therapy (RT) is used nowadays for the majority of cancer patients. A technologically advanced type of RT is IMRT – intensity-modulated radiation therapy. With this RT modality the cancerous cells of the patient can be irradiated using non-uniform radiation maps delivered from different Beam directions. Although non-uniform radiation maps allow, by themselves, an enhanced sparing of the neighboring healthy organs while properly irradiating the tumor with the prescribed dose, selection of appropriate irradiation directions play a decisive role on these conflicting tasks: deliver dose to the tumor while preventing (too much) dose to be deposited in the surrounding tissues. This paper focus on the problem of choosing the best set of irradiation directions, known as Beam Angle optimization (BAO) problem. Two completely different mathematical formulations of this problem can be found in the literature. A combinatorial formulation, widely used and addressed by many different algorithms and strategies, and a continuous formulation proposed by the authors and addressed by derivative-free algorithms. In this paper, a comparison of two of the most successful strategies to address each one of these formulations is done resorting to a set of ten clinical nasopharyngeal tumor cases already treated at the Portuguese Institute of Oncology of Coimbra.
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ICCSA (3) - A Global Score-Driven Beam Angle Optimization in IMRT
Computational Science and Its Applications – ICCSA 2017, 2017Co-Authors: Humberto Rocha, Tiago Ventura, Joana Dias, Brigida C. Ferreira, Maria Do Carmo LopesAbstract:Radiation therapy is one of the main treatment modalities for cancer. The objective of radiation therapy is to eliminate all cancer cells by delivering a prescribed dose of radiation to the tumor volume while sparing at the same time the surrounding tissues. Intensity-modulated radiation therapy (IMRT) is a sophisticated technologically-driven type of radiation therapy where non-uniform radiation fields are used to irradiate the patient from different Beam Angle directions. Appropriate selection of Beam irradiation directions – Beam Angle optimization (BAO) problem – enhance the quality of the treatment plan. The BAO problem is a very difficult global non-convex optimization problem for which there are few or none commercial solutions. Typically, the BAO procedure is driven by the outcome of the fluence map optimization (FMO) problem – the problem of calculating the most adequate radiation intensities. However, functions used for modeling the FMO problem have little clinical meaning. Typically, selection/validation of treatment plans is done considering a set of dosimetric measures. In this study, we propose a treatment plan global score, based on dosimetric criteria and its relative importance, as alternative plan’s quality measure to drive the BAO procedure. For the clinical case of nasopharyngeal tumor, the use of a global score to drive the BAO procedure lead to higher quality treatment plans. For similar target coverage, an improved organ sparing was obtained.
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ICCSA (1) - An Accelerated Multistart Derivative-Free Framework for the Beam Angle Optimization Problem in IMRT
Computational Science and Its Applications – ICCSA 2016, 2016Co-Authors: Humberto Rocha, Tiago Ventura, Joana Dias, Brigida C. Ferreira, Maria Do Carmo LopesAbstract:Radiation therapy, either alone or combined with surgery or chemotherapy, is one of the main treatment modalities for cancer. Intensity-modulated radiation therapy (IMRT) is an advanced form of radiation therapy, where the patient is irradiated using non-uniform radiation fields from selected Beam Angle directions. The goal of IMRT is to eradicate all cancer cells by delivering a radiation dose to the tumor volume, while attempting to spare, simultaneously, the surrounding organs and tissues. Although the use of non-uniform radiation fields can favor organ sparing, the selection of appropriate irradiation Beam Angle directions – Beam Angle optimization – is the best way to enhance organ sparing. The Beam Angle optimization (BAO) problem is an extremely challenging continuous non-convex multi-modal optimization problem. In this study, we present a novel approach for the resolution of the BAO problem, using a multistart derivative-free framework for a more thoroughly exploration of the search space of the highly non-convex BAO problem. As the objective function that drives the BAO problem is expensive in terms of computational time, and a multistart approach typically implies a large number of function evaluations, an accelerated framework is explored. A clinical case of an intra-cranial tumor treated at the Portuguese Institute of Oncology of Coimbra is used to discuss the benefits of the accelerated multistart approach proposed for the BAO problem.
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Noncoplanar Beam Angle optimization in IMRT treatment planning using pattern search methods
Journal of Physics: Conference Series, 2015Co-Authors: Humberto Rocha, Joana Dias, Brigida C. Ferreira, Maria Do Carmo LopesAbstract:Radiation therapy is used to treat localized cancers, aiming to deliver a dose of radiation to the tumor volume to sterilize all cancer cells while minimizing the collateral effects on the surrounding healthy organs and tissues. The planning of radiation therapy treatments requires decisions regarding the Angles used for radiation incidence, the fluence intensities and, if multileaf collimators are used, the definition of the leaf sequencing. The Beam Angle optimization problem consists in finding the optimal number and incidence directions of the irradiation Beams. The selection of appropriate radiation incidence directions is important for the quality of the treatment. However, the possibility of improving the quality of treatment plans by an optimized selection of the Beam incidences is seldom done in the clinical practice. Adding the possibility for noncoplanar incidences is even more rarely used. Nevertheless, the advantage of noncoplanar Beams is well known. The optimization of noncoplanar Beam incidences may further allow the reduction of the number of Beams needed to reach a clinically acceptable plan. In this paper we present the benefits of using pattern search methods for the optimization of the highly non-convex noncoplanar Beam Angle optimization problem.
Wufan Chen - One of the best experts on this subject based on the ideXlab platform.
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ant colony system for the Beam Angle optimization problem in radiotherapy planning a preliminary study
Congress on Evolutionary Computation, 2005Co-Authors: Yongjie Li, Wufan Chen, Jiancheng ZhengAbstract:Intensity-modulated radiotherapy (IMRT) is being increasingly used for treatment of malignant cancer. Beam Angle optimization (BAO) is an important problem in IMRT. In this paper, an emerging population-based meta-heuristic algorithm named ant colony optimization (ACO) is introduced to solve the BAO problem. In the proposed algorithm, a multi-layered graph is designed to map the BAO problem to ACO, and a heuristic function based on the Beam's-eye-view dosimetrics (BEVD) score is introduced. In order to verify the feasibility of the presented algorithm, a clinical prostate tumor case is employed, and the preliminary results demonstrate that ACO appears more effcient than genetic algorithm (GA) and can find the optimal Beam Angles within a clinically acceptable computation time.
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Congress on Evolutionary Computation - Ant colony system for the Beam Angle optimization problem in radiotherapy planning: a preliminary study
2005 IEEE Congress on Evolutionary Computation, 2005Co-Authors: Yongjie Li, Wufan Chen, Jiancheng ZhengAbstract:Intensity-modulated radiotherapy (IMRT) is being increasingly used for treatment of malignant cancer. Beam Angle optimization (BAO) is an important problem in IMRT. In this paper, an emerging population-based meta-heuristic algorithm named ant colony optimization (ACO) is introduced to solve the BAO problem. In the proposed algorithm, a multi-layered graph is designed to map the BAO problem to ACO, and a heuristic function based on the Beam's-eye-view dosimetrics (BEVD) score is introduced. In order to verify the feasibility of the presented algorithm, a clinical prostate tumor case is employed, and the preliminary results demonstrate that ACO appears more effcient than genetic algorithm (GA) and can find the optimal Beam Angles within a clinically acceptable computation time.
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Adaptive particle swarm optimizer for Beam Angle selection in radiotherapy planning
IEEE International Conference Mechatronics and Automation 2005, 2005Co-Authors: Dezhong Yao, Wufan ChenAbstract:As an emerging stochastic optimization paradigm, particle swarm optimization (PSO) algorithm has received a lot of attention in recent years. In this paper, a method named adaptive PSO is introduced to automatically select the Beam Angles for intensity-modulated radiotherapy (IMRT) planning. To date, the improvements in automatic Beam Angle selection are still not quite satisfying, especially on the optimization efficiency because of the handicap of huge hyperspace of solutions. In the proposed algorithm, the Beam Angles are selected using PSO, followed by a Beam intensity map optimization using conjugate gradient (CG) algorithm for each updated Beam configuration. This PSO-based algorithm is verified by a relatively complex clinical prostate tumor case. In addition, the efficiency is compared with a genetic algorithm (GA)-based approach. The preliminary results show that the proposed algorithm is feasible for the Beam Angle selection problem in IMRT planning. Furthermore, PSO appears more efficient than GA, according to the limited test case in this paper. Further study is needed to test the proposed method with more clinical cases.
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A particle swarm optimization algorithm for Beam Angle selection in intensity-modulated radiotherapy planning
Physics in Medicine and Biology, 2005Co-Authors: Dezhong Yao, Jonathan Yao, Wufan ChenAbstract:Automatic Beam Angle selection is an important but challenging problem for intensity-modulated radiation therapy (IMRT) planning. Though many efforts have been made, it is still not very satisfactory in clinical IMRT practice because of overextensive computation of the inverse problem. In this paper, a new technique named BASPSO (Beam Angle Selection with a Particle Swarm Optimization algorithm) is presented to improve the efficiency of the Beam Angle optimization problem. Originally developed as a tool for simulating social behaviour, the particle swarm optimization (PSO) algorithm is a relatively new population-based evolutionary optimization technique first introduced by Kennedy and Eberhart in 1995. In the proposed BASPSO, the Beam Angles are optimized using PSO by treating each Beam configuration as a particle (individual), and the Beam intensity maps for each Beam configuration are optimized using the conjugate gradient (CG) algorithm. These two optimization processes are implemented iteratively. The performance of each individual is evaluated by a fitness value calculated with a physical objective function. A population of these individuals is evolved by cooperation and competition among the individuals themselves through generations. The optimization results of a simulated case with known optimal Beam Angles and two clinical cases (a prostate case and a head-and-neck case) show that PSO is valid and efficient and can speed up the Beam Angle optimization process. Furthermore, the performance comparisons based on the preliminary results indicate that, as a whole, the PSO-based algorithm seems to outperform, or at least compete with, the GA-based algorithm in computation time and robustness. In conclusion, the reported work suggested that the introduced PSO algorithm could act as a new promising solution to the Beam Angle optimization problem and potentially other optimization problems in IMRT, though further studies need to be investigated.