IMRT Beam Angle Optimization Using Non-descent Pattern Search Methods

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IMRT beam angle optimization using electromagnetism-like algorithm

The selection of appropriate beam irradiation directions in radiotherapy – beam angle optimization (BAO) problem – is very impor- tant for the quality of the treatment, both for improving tumor irradia- tion and for better organs sparing. However, the BAO problem is still not solved satisfactorily and, most of the time, beam directions continue to be manually selected in clinical practice which requires many trial and error iterations between selecting beam angles and computing fluence patterns until a suitable treatment is achieved. The objective of this pa- per is to introduce a new approach for the resolution of the BAO problem, using an hybrid electromagnetism-like algorithm with descent search to tackle this highly non-convex optimization problem. Electromagnetism- like algorithms are derivative-free optimization methods with the ability to avoid local entrapment. Moreover, the hybrid electromagnetism-like algorithm with descent search has a high ability of producing descent directions. A set of retrospective treated cases of head-and-neck tumors at the Portuguese Institute of Oncology of Coimbra is used to discuss the benefits of the proposed algorithm for the optimization of the BAO problem.Fundação para a Ciência e a Tecnologia (FCT

Controlling Beam Complexity in Intensity Modulated Radiation Therapy.

External beam intensity modulated radiation therapy (IMRT) is a technique in which the spatial intensity of radiation from each beam direction can be modulated to provide superior conformality of dose to a tumor volume while sparing important normal tissues. A fundamental and potentially limiting feature of IMRT is the highly complex fields that can be created through inverse plan optimization. Highly modulated treatments are a large departure from conventional radiotherapy methods, are difficult to deliver accurately and efficiently, and can result in an undesirable increase in leakage dose being delivered to the patient. Longer deliveries may also increase the chance for patient motion during treatment and could potentially reduce the probability of controlling some tumors. The large intensity fluctuations observed in IMRT beams are often a result of the degeneracy of the optimization problem, and the types of optimization method and cost function used. This work demonstrates that beam complexity is a result of these two issues, and is dependent on the placement of dose evaluation points in the target and normal tissues. This research shows that (i) optimizing surfaces instead of discrete beamlet intensities to represent the beam can reduce the degrees of freedom in IMRT and results in much smoother beams at the expense of a slight increase in normal tissues, (ii) maximum beamlet intensity restrictions are useful for improved delivery efficiency, but may restrict the optimizer at low limits, and (iii) modulation penalties can be incorporated into the cost function to promote plan smoothness without sacrificing plan quality. Penalizing the overall plan modulation is an effective way to reduce modulation, but it falsely penalizes the desirable beam modulation as well as the undesirable modulation. To address this problem, diffusion principles are used to develop a spatially adaptive smoothing method that only penalizes the unnecessary beam modulation and can be used without degrading plan quality. This method is customizable to a variety of treatment scenarios. The clinical impact of reducing beam complexity is significant, as it can result in an improvement in delivery accuracy and efficiency, quicker optimization times, and increased robustness to point sampling and geometric uncertainty.Ph.D.Nuclear Engineering & Radiological SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/57648/2/mcoselmo_1.pd

Otimização angular com pesquisa Tabu em IMRT

Mestrado em Matemática e AplicaçõesO número de pacientes com cancro continua a crescer no mundo e a Organização Mundial da Saúde considerou mesmo esta como uma das principais ameaças para a saúde e o desenvolvimento humano. Dependendo da localização e das especi cidades do tumor, existem muitos tratamentos que podem ser usados, incluindo cirurgia, quimioterapia, imunoterapia e radioterapia. A Radioterapia de Intensidade Modulada (IMRT | Intensity Modulated Radiation Therapy) é uma das modalidades mais avançadas de radioterapia, onde a otimização pode ter um papel importante no que diz respeito à qualidade do tratamento aplicado. Em IMRT, o feixe de radiação pode ser visto como se fosse constituído por vários pequenos feixes, pelo uso de um colimador multifolhas, que permite que a intensidade seja modulada. Este complexo problema de otimização pode ser dividido em três subproblemas, que estão relacionados entre si e que podem ser resolvidos sequencialmente. Para cada paciente, os ângulos de onde a radiação ir a ocorrer têm de ser determinados (problema geométrico | otimização angular). Depois, para cada um desses ângulos, o mapa de intensidades (ou fluências) tem de ser calculado (problema das intensidades | otimização das fluências). Finalmente, e necessário determinar o comportamento do colimador multifolhas, de forma a garantir que as intensidades são, de facto, atribuídas (problema de realiza ção). Em cada um destes problemas de otimização, a qualidade do tratamento atribuído depende dos modelos e algoritmos usados. Neste trabalho, a nossa atenção estará particularmente focada na otimização angular, um problema conhecido por ser altamente não-convexo, com muitos mínimos locais e com uma função objetivo que requer muito tempo de computação para ser calculada. Tal significa, respetivamente, que os algoritmos que sejam baseados no cálculo de gradientes ou que requeiram muitas avaliações da função objetivo podem não ser adequados. Assim, os procedimentos metaheurísticos podem ser uma boa alternativa para abordar este problema, visto que são capazes de escapar de mínimos locais e são conhecidos por conseguirem calcular boas soluções em problemas complexos. Neste trabalho ser a descrita uma aplicação para Pesquisa Tabu. Serão ainda apresentados os testes computacionais realizados, considerando dez casos clínicos de pacientes previamente tratados por radioterapia, pretendendo-se mostrar que a Pesquisa Tabu e capaz de melhorar os resultados obtidos através da solução equidistante, cujo uso e comum na prática clínica.The number of cancer patients continues to grow worldwide and the World Health Organization has even considered cancer as one of the main threats to human health and development. Depending on the location and speci cities of the tumor, there are many treatments that can be used, including surgery, chemotherapy, immunotherapy and radiation therapy. Intensity Modulated Radiation Therapy (IMRT) is one of the most advanced radiation therapy modalities, and optimization can have a key role in the quality of the treatment delivered. In IMRT, the radiation beam can be thought of as being composed by several small beams, through the use of a multileaf collimator, allowing radiation intensity to be modulated. This complex optimization problem can be divided in three related subproblems that can be solved sequentially. For each patient, the angles from which the radiation will be delivered have to be determined (geometric problem | beam angle optimization). Then, for each of these angles, the radiation intensity map is calculated ( uence or intensity optimization). Finally, it is necessary to determine the behavior of the multileaf collimator that guarantees that the desired radiation intensities are, indeed, delivered (realization problem). In each of these optimization problems, the quality of the treatment delivered depends on the models and algorithms used. In this work the attention will be focused in beam angle optimization, a problem known to be highly non{convex, with many local minima and with an objective function that is time expensive to calculate, which, respectively, means that algorithms that are gradient{based or that require many objective function evaluations will not be adequate. Metaheuristics can be the right tool to tackle this problem, since they are capable of escaping local minima and are known to be able to calculate good solutions for complex problems. In this work, an application of Tabu Search to beam angle optimization is described. Computational results considering ten clinical cases of head{and{neck cancer patients are presented, showing that Tabu Search is capable of improving the equidistant solution usually used in clinical practice

Honey Yield Forecast Using Radial Basis Functions

Honey yields are difficult to predict and have been usually associated with weather conditions. Although some specific meteorological variables have been associated with honey yields, the reported relationships concern a specific geographical region of the globe for a given time frame and cannot be used for different regions, where climate may behave differently. In this study, Radial Basis Function (RBF) interpolation models were used to explore the relationships between weather variables and honey yields. RBF interpolation models can produce excellent interpolants, even for poorly distributed data points, capable of mimicking well unknown responses providing reliable surrogates that can be used either for prediction or to extract relationships between variables. The selection of the predictors is of the utmost importance and an automated forward-backward variable screening procedure was tailored for selecting variables with good predicting ability. Honey forecasts for Andalusia, the first Spanish autonomous community in honey production, were obtained using RBF models considering subsets of variables calculated by the variable screening procedure

Intensity Modulated Proton Therapy Optimization Under Uncertainty: Field Misalignment and Internal Organ Motion

Intensity modulated proton therapy (IMPT) is one of the most advanced forms of radiation therapy, which can deliver a highly conformal dose to the tumor while sparing the dose in healthy tissues. Compared to conventional photon-based radiation therapy, IMPT is more flexible in delivering radiation dose according to different tumor shapes. However, this flexibility also makes the optimization problems in IMPT harder to solve, e.g., it requires larger memory to store data and longer computational time. Furthermore, proton beams are very sensitive to different uncertainties, such as setup uncertainty, range uncertainty and internal organ motion. These uncertainties can greatly impact the quality of clinical treatment. Therefore, this dissertation aims to investigate different optimization methods for treatment planning and to handle a variety of uncertainties in IMPT. First, to solve the fluence map optimization (FMO) problem in IMPT, we propose a method to formulate the FMO problem into a molecular dynamics model. So that, the FMO problem can be optimized according classical dynamics system. This method combines the advantages of gradient-based algorithms and heuristic search algorithms. Next, we develop and validate a robust optimization method for IMPT treatment plans with multi-isocenter large fields to overcome the dose inhomogeneity problem caused by the setup misalignment in field junctions. Numerical results show that the robust optimized IMPT plans create a low gradient field radiation dose in the junction regions, which can minimize the impact from misalignment uncertainty. Compare to conventional techniques, the robust optimization method leads the whole treatment much more efficient. Lastly, we focus on a two-stage method to solve the beam angle optimization (BAO) problem in IMPT with internal organ motion uncertainty. In the first stage, a $p$-median algorithm is developed for beam angle clustering. In the second stage, a bi-level search algorithm is used to find the final beam angle set for the treatment. Furthermore, Support vector machine (SVM) is used for beam angle classification to reduce the search space and the 4D-CT information is incorporated to handle the internal organ motion uncertainty. Results show that the two-stage BAO method consistently finds a high-quality solution in a short time.Industrial Engineering, Department o

Managing radiotherapy treatment trade-offs using multi-criteria optimisation and data envelopment analysis

Techniques for managing trade-offs between tumour control and normal tissue sparing in radiotherapy treatment planning are reviewed and developed. Firstly, a quality control method based on data envelopment analysis is proposed. The method measures the improvement potential of a plan by comparing the plan to other reference plans. The method considers multiple criteria, including one that represents anatomical variations between patients. An application to prostate cases demonstrates the capability of the method in identifying plans with further improvement potential. A multi-criteria based planning technique that considers treatment delivery is then proposed. The method integrates column generation in the revised normal boundary intersection method, which projects a set of equidistant reference points onto the non-dominated set to form a representative set of non-dominated points. The delivery constraints are considered in the column generation process. Essentially, the method generates a set of deliverable plans featuring a range of treatment trade-offs. Demonstrated by a prostate case, the method generates near-optimal plans that can be delivered with dramatically lower total fluence than the optimal ones post-processed for treatment delivery constraints. Finally, a navigation method based on solving interactive multi-objective optimisation for a discrete set of plans is developed. The method sets the aspiration values for criteria as soft constraints, thus allowing the planner to freely express his/her preferences without causing infeasibility. Navigation is conducted on planner-defined clinical criteria, including the non-convex dose-volume criteria and treatment delivery time. Navigation steps on a prostate case are demonstrated with a prototype implementation. The prostate case shows that optimisation criteria may not correctly reflect plan quality and can mislead a planner to select a “sub-optimal” plan. Instead, using clinical criteria provides the most relevant measure of plan quality, hence allowing the planner to quickly identify the most preferable plan from a representative set