Hybrid Classical-quantum Dose Computation Method for Radiation Therapy Treatment Planning PDF Download
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Author: Gabriel G. Colburn
Publisher:
ISBN:
Category : Monte Carlo method
Languages : en
Pages : 100
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Book Description
Radiation therapy treatment planning and optimization requires accurate, precise, and fast computation of absorbed dose to all critical and target volumes in a patient. A new method for speeding up the computational costs of Monte Carlo dose calculations is described that employs a hybrid classical-quantum computing architecture. Representative results are presented from computer simulations using modified and unmodified versions of the Dose Planning Method (DPM) Monte Carlo code. The architecture and methods may be extended to sampling arbitrary discrete probability density functions using quantum bits with application to many fields.
Author: Gabriel G. Colburn
Publisher:
ISBN:
Category : Monte Carlo method
Languages : en
Pages : 100
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Book Description
Radiation therapy treatment planning and optimization requires accurate, precise, and fast computation of absorbed dose to all critical and target volumes in a patient. A new method for speeding up the computational costs of Monte Carlo dose calculations is described that employs a hybrid classical-quantum computing architecture. Representative results are presented from computer simulations using modified and unmodified versions of the Dose Planning Method (DPM) Monte Carlo code. The architecture and methods may be extended to sampling arbitrary discrete probability density functions using quantum bits with application to many fields.
Author: Mat Mustafa Tamimi
Publisher:
ISBN:
Category : Cancer
Languages : en
Pages : 163
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Book Description
A successful radiation therapy treatment aims at conforming (i.e., concentrating) radiation dose to the entire tumor volume (i.e., diseased area) while avoiding surrounding normal tissue (i.e., healthy non-diseased areas). This objective is achieved clinically by finding a set of radiation beam parameters that successfully deliver the desired dose distribution. In this project, a hybrid forward/adjoint Monte Carlo based absorbed dose computation method is developed and tested, aimed at eventual implementation in a radiation therapy external beam treatment planning system to predict the absorbed dose produced by a medical linear accelerator. This absorbed dose calculational engine was designed to be:1. Efficient. This is achieved by incorporating several Monte Carlo techniques used in the Nuclear Engineering field for deep penetration and reactor analysis problem. 2. Flexible. This is achieved by using a Cartesian grid and a voxelized material map. Currently most of the absorbed dose calculation algorithms in radiotherapy are 3-D based predictive models. The use of such algorithms results in treatment planning quality that depends tremendously on the planner’s experience and knowledge base. This dependence, along with inaccuracy in predicting absorbed dose due to the assumptions and simplifications used in these algorithms, can result in a predicted absorbed dose that under- or over-predicts the delivered dose. As an alternative, forward and adjoint Monte Carlo absorbed dose computation methods have been used and validated by several authors (Difilippo, 1998; Goldstein & Regev, 1999; Jeraj & Keall, 1999). However, in the “pure” forward or adjoint methods, each change in the radiation beam parameters requires its own time-consuming 3D calculation; for the hybrid technique developed in this research, a single 3D calculation for each desired dose region (tumor or healthy organ) is all that is required. This project also improves the Monte Carlo methodology by incorporating the use of voxelized fictitious scattering and surface forward/adjoint coupling. The accuracy is demonstrated through comparison with forward and adjoint MCNP calculations of a simple beam/patient sample problem.
Author: Ryan Neph
Publisher:
ISBN:
Category :
Languages : en
Pages : 156
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Book Description
Radiation therapy is powered by modern techniques in precise planning and execution of radiation delivery, which are being rapidly improved to maximize its benefit to cancer patients. In the last decade, radiotherapy experienced the introduction of advanced methods for automatic beam orientation optimization, real-time tumor tracking, daily plan adaptation, and many others, which improve the radiation delivery precision, planning ease and reproducibility, and treatment efficacy. However, such advanced paradigms necessitate the calculation of orders of magnitude more causal dose deposition data, increasing the time requirement of all pre-planning dose calculation. Principles of high-performance computing and machine learning were applied to address the insufficient speeds of widely-used dose calculation algorithms to facilitate translation of these advanced treatment paradigms into clinical 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 its novel implementation on highly parallelized GPUs. This context-based GPU-CCCS approach takes advantage of X-ray dose deposition compactness to parallelize calculation across hundreds of beamlets, reducing hardware-specific overheads, and enabling acceleration by two to three orders of magnitude compared to existing GPU-based beamlet-by-beamlet approaches. Near-linear increases in acceleration are achieved with a distributed, multi-GPU implementation 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. EREs necessitate the use of much slower Monte Carlo (MC) dose calculation, limiting the clinical application of advanced treatment paradigms due to time restrictions. An automatically distributed framework for very-large-scale MC dose calculation was developed, granting linear scaling of dose calculation speed with the number of utilized computational cores. It was then harnessed to efficiently generate a large dataset of paired high- and low-noise MC doses in a 1.5 tesla magnetic field, which were used to train a novel deep convolutional neural 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 dose calculation, while remaining synergistic with existing MC acceleration techniques to achieve multiplicative speed improvements. This work redefines the expectation of X-ray dose calculation speed, making it possible to apply new highly-beneficial treatment paradigms to standard clinical practice for the first time.
Author: Issam El Naqa
Publisher: Springer
ISBN: 3319183052
Category : Medical
Languages : en
Pages : 336
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Book Description
This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
Author: Elinore Wieslander
Publisher:
ISBN: 9789162866754
Category :
Languages : en
Pages : 53
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Author: Jahangir Ahmad Satti
Publisher:
ISBN:
Category :
Languages : en
Pages : 216
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Author: Yan Jiang Graves
Publisher:
ISBN: 9781303620195
Category :
Languages : en
Pages : 163
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Book Description
Radiation therapy aims at delivering a prescribed amount of radiation dose to cancerous targets while sparing dose to normal organs. Treatment planning and delivery in modern radiotherapy are highly complex. To ensure the accuracy of the delivered dose to a patient, a quality assurance (QA) procedure is needed before the actual treatment delivery. This dissertation aims at developing computational and physical tools to facilitate the QA process. In Chapter 2, we have developed a fast and accurate computational QA tool using a graphics processing unit based Monte Carlo (MC) dose engine. This QA tool aims at identifying any errors in the treatment planning stage and machine delivery process by comparing three dose distributions: planned dose computed by a treatment planning system, planned dose and delivered dose reconstructed using the MC method. Within this tool, several modules have been built. (1) A denoising algorithm to smooth the MC calculated dose. We have also investigated the effects of statistical uncertainty in MC simulations on a commonly used dose comparison metric. (2) A linear accelerator source model with a semi-automatic commissioning process. (3) A fluence generation module. With all these modules, a web application for this QA tool with a user friendly interface has been developed to provide users with easy access to our tool, facilitating its clinical utilizations. Even after an initial treatment plan fulfills the QA requirements, a patient may experience inter-fractional anatomy variations, which compromise the initial plan optimality. To resolve this issue, adaptive radiotherapy (ART) has been proposed, where treatment plan is redesigned based on most recent patient anatomy. In Chapter 3, we have constructed a physical deformable head and neck (HN) phantom with in-vivo dosimetry capability. This phantom resembles HN patient geometry and simulates tumor shrinkage with a high level of realism. The ground truth deformation field can be measured from built-in surface markers, which is then used to verify the accuracy of an important ART step of deformable image registration. Our experiments also demonstrate the feasibility of using this phantom as an end-to-end ART QA phantom with an emphasis on testing the dose deliver accuracy.
Author: James W. Longacre
Publisher:
ISBN:
Category : Cancer
Languages : en
Pages : 102
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Author: Angelia Landers
Publisher:
ISBN:
Category :
Languages : en
Pages : 143
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Book Description
Intensity-modulated radiotherapy treatment planning is an inverse problem that typically includes numerous parameters that have to be manually tuned by expert planners. This process can take hours or even days and can often lead to suboptimal plans. In this study, we developed a technique for fully automated radiotherapy treatment planning with the guidance of dose predictions using high quality or evolving knowledge bases. Knowledge-based planning (KBP) dose prediction provides patient-specific estimations for the capabilities and limitations of a plan. Statistical voxel dose learning (SVDL) was developed to predict the voxel dose of new patients. The method was compared to supervised machine learning methods, spectral regression (SR) and support vector regression (SVR), to evaluate the prediction accuracy and robustness of using small training sets. SVDL was found to have higher prediction accuracy than the more sophisticated machine learning methods and effective even with small training sets. To remove any dependence on hyperparameters that require manual tuning, voxel-based non-coplanar 4 radiotherapy and coplanar volumetric modulated arc therapy (VMAT) optimization problems were modified to include the KBP predicted doses. The new cost functions encourage the plans to meet or improve on the predicted doses. Because of this, the resulting plan quality is heavily reliant on the plan quality of the KBP training set. To ensure high quality plans, non-coplanar and coplanar IMRT plans were manually created using all available beams. The resulting automated plans were of superior quality compared to manually-created plans. In the case of no existing high quality training set, evolving-knowledge-base (EKB) planning was developed. An initial, low quality training set was used for the first epoch of automated planning. In subsequent epochs, the superior plans from the previous epoch were taken as the training set. Overall plan quality was observed to improve through epochs, plateauing after 3 and 6 epochs for lung and head & neck planning, respectively. The final EKB plans were significantly higher quality than manually-created VMAT plans and equivalent to manually-created 4 plans. Through the course of this work, we established a robust and accurate KBP dose prediction technique, which we then utilized in our automated planning protocol. Both the use of high quality training sets and EKB planning created high quality plans in a more efficient and consistent manner than hyperparameter tuning.
Author: Robert van der Laarse
Publisher:
ISBN:
Category :
Languages : en
Pages : 174
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Book Description
