Accelerating Radiation Dose Calculation with High Performance Computing and Machine Learning for Large-scale Radiotherapy Treatment Planning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Accelerating Radiation Dose Calculation with High Performance Computing and Machine Learning for Large-scale Radiotherapy Treatment Planning write by Ryan Neph. This book was released on 2020. Accelerating Radiation Dose Calculation with High Performance Computing and Machine Learning for Large-scale Radiotherapy Treatment Planning available in PDF, EPUB and Kindle. 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.
Machine Learning in Radiation Oncology
Machine Learning in Radiation Oncology - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning in Radiation Oncology write by Issam El Naqa. This book was released on 2015-06-19. Machine Learning in Radiation Oncology available in PDF, EPUB and Kindle. 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.
Machine Learning With Radiation Oncology Big Data
Machine Learning With Radiation Oncology Big Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning With Radiation Oncology Big Data write by Jun Deng. This book was released on 2019-01-21. Machine Learning With Radiation Oncology Big Data available in PDF, EPUB and Kindle.
Image-Based Computer-Assisted Radiation Therapy
Image-Based Computer-Assisted Radiation Therapy - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Image-Based Computer-Assisted Radiation Therapy write by Hidetaka Arimura. This book was released on 2017-01-26. Image-Based Computer-Assisted Radiation Therapy available in PDF, EPUB and Kindle. This book provides a comprehensive overview of the state-of-the-art computational intelligence research and technologies in computer-assisted radiation therapy based on image engineering. It also traces major technical advancements and research findings in the field of image-based computer-assisted radiation therapy. In high-precision radiation therapies, novel approaches in image engineering including computer graphics, image processing, pattern recognition, and computational anatomy play important roles in improving the accuracy of radiation therapy and assisting decision making by radiation oncology professionals, such as radiation oncologists, radiation technologists, and medical physicists, in each phase of radiation therapy. All the topics presented in this book broaden understanding of the modern medical technologies and systems for image-based computer-assisted radiation therapy. Therefore this volume will greatly benefit not only radiation oncologists and radiologists but also radiation technologists, professors in medical physics or engineering, and engineers involved in the development of products to utilize this advanced therapy.
Treatment Planning and Dose Calculation in Radiation Oncology
Treatment Planning and Dose Calculation in Radiation Oncology - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Treatment Planning and Dose Calculation in Radiation Oncology write by Gunilla C. Bentel. This book was released on 2014-02-20. Treatment Planning and Dose Calculation in Radiation Oncology available in PDF, EPUB and Kindle. Treatment Planning and Dose Calculation in Radiation Oncology, Third Edition describes the treatment methods and technical guides as models of contemporary radiation therapy. These models should be modified for each individual patient to yield a best fit to the disease being treated and the radiation sources employed. This book is composed of seven chapters, and begins with an overview of the elements of clinical radiation oncology. The subsequent chapter deals with the production, interaction, and measurement of radiation. These topics are followed by intensive discussions of dose calculation for external beams and pretreatment procedures of radiation therapy. A chapter looks into the principles, apparatus, and dose calculation in brachytherapy. The final chapters describe the principles and practical applications of treatment planning. This book will be of value to radiation oncologists.
