111 research outputs found
Beam Orientation Optimization for Intensity Modulated Radiation Therapy using Adaptive l1 Minimization
Beam orientation optimization (BOO) is a key component in the process of IMRT
treatment planning. It determines to what degree one can achieve a good
treatment plan quality in the subsequent plan optimization process. In this
paper, we have developed a BOO algorithm via adaptive l_1 minimization.
Specifically, we introduce a sparsity energy function term into our model which
contains weighting factors for each beam angle adaptively adjusted during the
optimization process. Such an energy term favors small number of beam angles.
By optimizing a total energy function containing a dosimetric term and the
sparsity term, we are able to identify the unimportant beam angles and
gradually remove them without largely sacrificing the dosimetric objective. In
one typical prostate case, the convergence property of our algorithm, as well
as the how the beam angles are selected during the optimization process, is
demonstrated. Fluence map optimization (FMO) is then performed based on the
optimized beam angles. The resulted plan quality is presented and found to be
better than that obtained from unoptimized (equiangular) beam orientations. We
have further systematically validated our algorithm in the contexts of 5-9
coplanar beams for 5 prostate cases and 1 head and neck case. For each case,
the final FMO objective function value is used to compare the optimized beam
orientations and the equiangular ones. It is found that, our BOO algorithm can
lead to beam configurations which attain lower FMO objective function values
than corresponding equiangular cases, indicating the effectiveness of our BOO
algorithm.Comment: 19 pages, 2 tables, and 5 figure
Beam selection for stereotactic ablative radiotherapy using Cyberknife with multileaf collimation.
The Cyberknife system (Accuray Inc., Sunnyvale, CA) enables radiotherapy using stereotactic ablative body radiotherapy (SABR) with a large number of non-coplanar beam orientations. Recently, a multileaf collimator has also been available to allow flexibility in field shaping. This work aims to evaluate the quality of treatment plans obtainable with the multileaf collimator. Specifically, the aim is to find a subset of beam orientations from a predetermined set of candidate directions, such that the treatment quality is maintained but the treatment time is reduced. An evolutionary algorithm is used to successively refine a randomly selected starting set of beam orientations. By using an efficient computational framework, clinically useful solutions can be found in several hours. It is found that 15Â beam orientations are able to provide treatment quality which approaches that of the candidate beam set of 110Â beam orientations, but with approximately half of the estimated treatment time. Choice of an efficient subset of beam orientations offers the possibility to improve the patient experience and maximise the number of patients treated
New concepts for beam angle selection in IMRT treatment planning : From heuristics to combinatorial optimization
This thesis investigates beam ensemble selection strategies in intensity-modulated radiation therapy treatment planning. Beam ensemble selection strategies are applied to find the very beam ensembles that meet the treatments' objectives at the best possible rate. (1) A formal description of the beam ensemble selection problem is presented and the characteristics of the search space is discussed with a focus on its non-convexity and exponential complexity. (2) We review existing approaches to beam ensemble selection and provide a comprehensive overview of the field. (3) Conceptual advancements of beam ensemble selection strategies relying on score functions and geometric considerations are introduced. For photons, we demonstrate a clear benefit regarding organ at risk sparing for asymmetric patient geometries as regularly observed within the abdomen or skull. For protons, phantom studies yield plausible beam configurations. The measures taken to guarantee robustness regarding potential uncertainties are promising but require refinements. (4) The simultaneous optimization of beamlet weights and beam orientations is investigated at a very high precision. We apply different metaheuristics for the combinatorial optimization of beam ensembles and confirm the beneficial performance of genetic algorithms in this context. Both heuristic selection and combinatorial optimization of beam ensembles may yield extensive benefits for complicated planning cases. In the future it will be critical to transfer automated beam ensemble selection to the clinic for the benefit of the patient
Depth Modulation in Radiotherapy
Intensity Modulated Radiotherapy (IMRT) has been a major field of research over the last thirty years and is today the standard in radiotherapy treatment of cancer. The introduction of IMRT into the clinical environment has greatly improved the ability of the treatment team to conform the radiation dose to the tumour volume. Alongside improvements in image guidance, IMRT has led to a reduction in side effects for patients and opened up the possibilities of dose escalation and hypofractionation. IMRT is however by no means perfect. IMRT and derivatives such as Volumated Arc Therapy (VMAT) are limited by the exit dose from the X-ray beams and deliver a significant amount of radiation dose to normal tissues. The much publicised alternative to IMRT is proton therapy. Proton therapy beams deposit dose over a narrow range resulting in minimal exit dose. The future of radiotherapy certainly involves a significant contribution from proton therapy but the availability to patients is likely to remain limited for a long time to come. The research in this thesis considers the possibility of further improving IMRT by modulating radiotherapy beams along their direction of travel as well as across their intensity, i.e. the so called ‘Depth Modulation’ of the thesis title. Although there are numerous possible ways to achieve depth modulation, this work proposes a combination of X-ray beams with electron beams of different energies with both modalities delivered with a conventional medical linear accelerator. The research in this thesis is concerned with developing a proof of principle for this method. It is to some extent a theoretical study, however at each step the possibility of practical implementation has been considered with the view that the method is only a viable proposition if it can be effectively implemented into clinical practice.
The technique proposed in this work is to use electron beams delivered through X-ray MLC with a standard patient set up. To reduce scatter and photon contamination it is proposed to remove the scattering foils from the beamline and to employ optimisation of the electron and photon components to compensate for any remaining penumbra broadening. The research has shown that improvements to dosimetry through removal of the scattering foil would allow delivery without reducing the source to surface distance, making a single isocentre synergistic delivery for both the electron and photon components practical. Electron dose segments have been calculated using Monte Carlo radiation transport and a procedure to optimise dose for the combined photon and electron IMRT technique has been developed. Through development of the optimisation procedure the characteristics of the mixed modality technique have been examined. A number of findings are demonstrated such as the benefit of gaps between electron segments, the benefits of optimising for energy in three dimensions and the dependence of the cost function minimum on the electron to photon ratio. Through clinical examples it has been shown that for tumours close to the surface the mixed modality technique has the potential to reduce the dose to normal tissues, particular in the low dose wash. Calculations of relative malignant induction probability demonstrate that this reduction in dose has the potential to reduce the incidence of secondary cancer induction. Possible treatment sites for application of the technique include breast, head and neck, brain and sarcomas
Towards on-line plan adaptation of unified intensity-modulated arc therapy using a fast-direct aperture optimization algorithm
External beam radiotherapy (EBRT) plays a vital role in the treatment of cancer, with close to half of all cancer patients receiving EBRT at some point over their course of treatment. Although EBRT is a well-established form of treatment, there are a number of ways in which EBRT could still be improved in terms of quality and efficiency for treatment planning and radiation dose delivery. This thesis reports a series of improvements made to EBRT.
First, we developed and evaluated a new treatment planning technique called unified intensity-modulated arc therapy (UIMAT) which combines the optimization and delivery of rotational volumetric modulated arc therapy (VMAT) and fixed-gantry intensity-modulated radiation therapy (IMRT). When retrospectively compared to clinical treatment plans using VMAT or IMRT alone, UIMAT plans reduced the dose to nearby critical structures by as much as 23% without compromising tumour volume coverage. The UIMAT plans were also more efficient to deliver. The reduction in normal tissue dose could help lower the probability of treatment-related toxicities, or alternatively could be used to improve tumour control probability, via dose escalation, while maintaining current dose limits for organs at risk.
Second, we developed a new fast inverse direct aperture optimization (FIDAO) algorithm for IMRT, VMAT, and UIMAT treatment planning. FIDAO introduces modifications to the direct aperture optimization (DAO) process that help improve its computational efficiency. As demonstrated in several test cases, these modifications do not significantly impact the plan quality but reduced the DAO time by as much as 200-fold. If implemented with graphical processing units (GPUs), this project may allow for applications such as on-line treatment adaptation.
Third, we investigated a method of acquiring tissue density information from cone-beam computed tomography (CBCT) datasets for on-line dose calculations, plan assessment, and potentially plan adaptation using FIDAO. This calibration technique accounts for patient-specific scattering conditions, demonstrated high dosimetric accuracy, and can be easily automated for on-line plan assessment.
Collectively, these three projects will help reduce the normal tissue doses from EBRT, improve the planning and delivery efficiency, and pave the way for application like on-line plan assessment and adaptive radiotherapy in response to anatomical changes
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Accelerating Radiation Dose Calculation with High Performance Computing and Machine Learning for Large-scale Radiotherapy Treatment Planning
Radiation therapy is powered by modern techniques in precise planning and executionof radiation delivery, which are being rapidly improved to maximize its benefit to cancerpatients. In the last decade, radiotherapy experienced the introduction of advanced methodsfor automatic beam orientation optimization, real-time tumor tracking, daily planadaptation, and many others, which improve the radiation delivery precision, planning easeand reproducibility, and treatment efficacy. However, such advanced paradigms necessitatethe calculation of orders of magnitude more causal dose deposition data, increasing the timerequirement of all pre-planning dose calculation. Principles of high-performance computingand machine learning were applied to address the insufficient speeds of widely-used dosecalculation algorithms to facilitate translation of these advanced treatment paradigms intoclinical practice.To accelerate CT-guided X-ray therapies, Collapsed-Cone Convolution-Superposition(CCCS), a state-of-the-art analytical dose calculation algorithm, was accelerated through itsnovel implementation on highly parallelized GPUs. This context-based GPU-CCCS approachtakes advantage of X-ray dose deposition compactness to parallelize calculation acrosshundreds of beamlets, reducing hardware-specific overheads, and enabling acceleration bytwo to three orders of magnitude compared to existing GPU-based beamlet-by-beamletapproaches. Near-linear increases in acceleration are achieved with a distributed, multi-GPUimplementation of context-based GPU-CCCS.Dose calculation for MR-guided treatment is complicated by electron return effects(EREs), exhibited by ionizing electrons in the strong magnetic field of the MRI scanner. EREsnecessitate the use of much slower Monte Carlo (MC) dose calculation, limiting the clinicalapplication of advanced treatment paradigms due to time restrictions. An automaticallydistributed framework for very-large-scale MC dose calculation was developed, grantinglinear scaling of dose calculation speed with the number of utilized computational cores. Itwas then harnessed to efficiently generate a large dataset of paired high- and low-noise MCdoses in a 1.5 tesla magnetic field, which were used to train a novel deep convolutionalneural network (CNN), DeepMC, to predict low-noise dose from faster high-noise MC-simulation. DeepMC enables 38-fold acceleration of MR-guided X-ray beamlet dosecalculation, while remaining synergistic with existing MC acceleration techniques to achievemultiplicative speed improvements.This work redefines the expectation of X-ray dose calculation speed, making it possibleto apply new highly-beneficial treatment paradigms to standard clinical practice for the firsttime
Multi-criteria optimization algorithms for high dose rate brachytherapy
L’objectif général de cette thèse est d’utiliser les connaissances en physique de la radiation, en programmation informatique et en équipement informatique à la haute pointe de la technologie pour améliorer les traitements du cancer. En particulier, l’élaboration d’un plan de traitement en radiothérapie peut être complexe et dépendant de l’utilisateur. Cette thèse a pour objectif de simplifier la planification de traitement actuelle en curiethérapie de la prostate à haut débit de dose (HDR). Ce projet a débuté à partir d’un algorithme de planification inverse largement utilisé, la planification de traitement inverse par recuit simulé (IPSA). Pour aboutir à un algorithme de planification inverse ultra-rapide et automatisé, trois algorithmes d’optimisation multicritères (MCO) ont été mis en oeuvre. Suite à la génération d’une banque de plans de traitement ayant divers compromis avec les algorithmes MCO, un plan de qualité a été automatiquement sélectionné. Dans la première étude, un algorithme MCO a été introduit pour explorer les frontières de Pareto en curiethérapie HDR. L’algorithme s’inspire de la fonctionnalité MCO intégrée au système Raystation (RaySearch Laboratories, Stockholm, Suède). Pour chaque cas, 300 plans de traitement ont été générés en série pour obtenir une approximation uniforme de la frontière de Pareto. Chaque plan optimal de Pareto a été calculé avec IPSA et chaque nouveau plan a été ajouté à la portion de la frontière de Pareto où la distance entre sa limite supérieure et sa limite inférieure était la plus grande. Dans une étude complémentaire, ou dans la seconde étude, un algorithme MCO basé sur la connaissance (kMCO) a été mis en oeuvre pour réduire le temps de calcul de l’algorithme MCO. Pour ce faire, deux stratégies ont été mises en oeuvre : une prédiction de l’espace des solutions cliniquement acceptables à partir de modèles de régression et d’un calcul parallèle des plans de traitement avec deux processeurs à six coeurs. En conséquence, une banque de plans de traitement de petite taille (14) a été générée et un plan a été sélectionné en tant que plan kMCO. L’efficacité de la planification et de la performance dosimétrique ont été comparées entre les plans approuvés par le médecin et les plans kMCO pour 236 cas. La troisième et dernière étude de cette thèse a été réalisée en coopération avec Cédric Bélanger. Un algorithme MCO (gMCO) basé sur l’utilisation d’un environnement de développement compatible avec les cartes graphiques a été mis en oeuvre pour accélérer davantage le calcul. De plus, un algorithme d’optimisation quasi-Newton a été implémenté pour remplacer le recuit simulé dans la première et la deuxième étude. De cette manière, un millier de plans de traitement avec divers compromis et équivalents à ceux générés par IPSA ont été calculés en parallèle. Parmi la banque de plans de traitement généré par l’agorithme gMCO, un plan a été sélectionné (plan gMCO). Le temps de planification et les résultats dosimétriques ont été comparés entre les plans approuvés par le médecin et les plans gMCO pour 457 cas. Une comparaison à grande échelle avec les plans approuvés par les radio-oncologues montre que notre dernier algorithme MCO (gMCO) peut améliorer l’efficacité de la planification du traitement (de quelques minutes à 9:4 s) ainsi que la qualité dosimétrique des plans de traitements (des plans passant de 92:6% à 99:8% selon les critères dosimétriques du groupe de traitement oncologique par radiation (RTOG)). Avec trois algorithmes MCO mis en oeuvre, cette thèse représente un effort soutenu pour développer un algorithme de planification inverse ultra-rapide, automatique et robuste en curiethérapie HDR.The overall purpose of this thesis is to use the knowledge of radiation physics, computer programming and computing hardware to improve cancer treatments. In particular, designing a treatment plan in radiation therapy can be complex and user-dependent, and this thesis aims to simplify current treatment planning in high dose rate (HDR) prostate brachytherapy. This project was started from a widely used inverse planning algorithm, Inverse Planning Simulated Annealing (IPSA). In order to eventually lead to an ultra-fast and automatic inverse planning algorithm, three multi-criteria optimization (MCO) algorithms were implemented. With MCO algorithms, a desirable plan was selected after computing a set of treatment plans with various trade-offs. In the first study, an MCO algorithm was introduced to explore the Pareto surfaces in HDR brachytherapy. The algorithm was inspired by the MCO feature integrated in the Raystation system (RaySearch Laboratories, Stockholm, Sweden). For each case, 300 treatment plans were serially generated to obtain a uniform approximation of the Pareto surface. Each Pareto optimal plan was computed with IPSA, and each new plan was added to the Pareto surface portion where the distance between its upper boundary and its lower boundary was the largest. In a companion study, or the second study, a knowledge-based MCO (kMCO) algorithm was implemented to shorten the computation time of the MCO algorithm. To achieve this, two strategies were implemented: a prediction of clinical relevant solution space with previous knowledge, and a parallel computation of treatment plans with two six-core CPUs. As a result, a small size (14) plan dataset was created, and one plan was selected as the kMCO plan. The planning efficiency and the dosimetric performance were compared between the physician-approved plans and the kMCO plans for 236 cases. The third and final study of this thesis was conducted in cooperation with Cédric Bélanger. A graphics processing units (GPU) based MCO (gMCO) algorithm was implemented to further speed up the computation. Furthermore, a quasi-Newton optimization engine was implemented to replace simulated annealing in the first and the second study. In this way, one thousand IPSA equivalent treatment plans with various trade-offs were computed in parallel. One plan was selected as the gMCO plan from the calculated plan dataset. The planning time and the dosimetric results were compared between the physician-approved plans and the gMCO plans for 457 cases. A large-scale comparison against the physician-approved plans shows that our latest MCO algorithm (gMCO) can result in an improved treatment planning efficiency (from minutes to 9:4 s) as well as an improved treatment plan dosimetric quality (Radiation Therapy Oncology Group (RTOG) acceptance rate from 92.6% to 99.8%). With three implemented MCO algorithms, this thesis represents a sustained effort to develop an ultra-fast, automatic and robust inverse planning algorithm in HDR brachytherapy
A case-based reasoning system for radiotherapy treatment planning for brain cancer
In this thesis, a novel case-based reasoning (CBR) approach to radiotherapy treatment planning for brain cancer patients is presented. In radiotherapy, tumour cells are destroyed using ionizing radiation. For each patient, a treatment plan is generated that describes how the radiation should be applied in order to deliver a tumouricidal radiation dose while avoiding irradiation of healthy tissue and organs at risk in the vicinity of the tumour. The traditional, manual trial and error approach is a time-consuming process that depends on the experience and intuitive knowledge of medical physicists. CBR is an artificial intelligence methodology, which attempts to solve new problems based on the solutions of previously solved similar problems. In this research work, CBR is used to generate the parameters of a treatment plan by capturing the subjective and intuitive knowledge of expert medical physicists stored intrinsically in the treatment plans of similar patients treated in the past.
This work focusses on the retrieval stage of the CBR system, in which given a new patient case, the most similar case in the archived case base is retrieved along with its treatment plan. A number of research issues that arise from using CBR for radiotherapy treatment planning for brain cancer are addressed. Different approaches to similarity calculation between cases are investigated and compared, in particular, the weighted nearest neighbour similarity measure and a novel non-linear, fuzzy similarity measure designed for our CBR system. A local case attribute weighting scheme has been developed that uses rules to assign attribute weights based on the values of the attributes in the new case and is compared to global attribute weighting, where the attribute weights remain constant for all target cases. A multi-phase case retrieval approach is introduced in which each phase considers one part of the solution. In addition, a framework developed for the imputation of missing values in the case base is described.
The research was carried out in collaboration with medical physicists at the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK. The performance of the developed methodologies was tested using brain cancer patient cases obtained from the City Hospital. The results obtained show that the success rate of the retrieval mechanism provides a good starting point for adaptation, the next phase in development for the CBR system. The developed automated CBR system will assist medical physicists in quickly generating treatment plans and can also serve as a teaching and training aid for junior, inexperienced medical physicists. In addition, the developed methods are generic in nature and can be adapted to be used in other CBR or intelligent decision support systems for other complex, real world, problem domains that highly depend on subjective and intuitive knowledge
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