Investigation Of Adaptive Radiation Therapy Including Deformable Image Registration Treatment Planning Modification Strategies Machine Learning Deep Learning
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Investigation Of Adaptive Radiation Therapy Including Deformable Image Registration Treatment Planning Modification Strategies Machine Learning Deep Learning
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Author : Pawel Siciarz
language : en
Publisher:
Release Date : 2021
Investigation Of Adaptive Radiation Therapy Including Deformable Image Registration Treatment Planning Modification Strategies Machine Learning Deep Learning written by Pawel Siciarz and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
The goal of this research was to propose and evaluate solutions to four important aspects of adaptive radiation therapy in order to make it more reliable, accurate, and efficient in clinical environment. The first study focused on the evaluation of several deformable image registration algorithms. Results demonstrated that the Dense Anatomical Block Matching registration outperformed the other methods making it a very promising alternative to the existing registration methods for challenging CT-to-CBCT registration and its applications for radiation dose calculation, dose mapping and contour propagation in adaptive radiation therapy (ART) of the pelvic region. The second study focused on the quantitative evaluation of eight proposed adaptive radiation therapy approaches for prostate cancer patients treated with hypofractionated VMAT. The ART strategies included online and offline methods. The comprehensive analysis showed that daily on-line adaptation approaches were the most impactful. The findings of this study provided applicable insights into the selection of the optimal ART strategy, improving the quality of the decision-making process based on the quantitatively evaluated dosimetric benefits. The third study aimed to utilize a deep learning network to automatically contour critical organs on the computed tomography (CT) scans of head and neck cancer patients. Proposed model achieved expert level accuracy and was able to segment 25 critical organs on unseen CT images in approximately 7 seconds per patient. High accuracy and short contouring time allow for the implementation of the model within a clinical ART workflow, which would lead to a significant decrease in the time required to create a new adapted treatment plan. The objective of the fourth study was to use artificial intelligence methods to build a decision making support system that would classify previously delivered plans of brain tumor patients into those that met treatment planning objectives and those for which objectives were not met due to the priority given to one or more organs-at-risk. Among evaluated machine learning algorithms, the Logistic Regression model achieved the highest accuracy and can be used by radiation oncologists to support their decision-making process in terms of treatment plan adaptations and plan approvals in a data-driven quality assurance program.
Strategies For Adaptive Radiation Therapy
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Author : Junyi Xia
language : en
Publisher:
Release Date : 2009
Strategies For Adaptive Radiation Therapy written by Junyi Xia and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
ABSTRACT: Image guided radiation therapy (IGRT) requires developing advanced methods for target localization. Once target motion is identified, the patient specific treatment margin can be incorporated into the treatment planning, accurately delivering the radiation dose to the target and minimizing the dose to the normal tissues. Deformable image registration (DIR) has become an indispensable tool to analyze target motion and measure physiological change by temporal imaging or time series volumetric imaging, such as four-dimensional computed tomography (4DCT). Current DIR algorithms suffer from inverse inconsistency, where the deformation mapping is not unique after switching the order of the images. Moreover, long computation time of current DIR implementation limits its clinical application to offline analysis. This dissertation makes several major contributions: First, an inverse consistent constraint (ICC) is proposed to constrain the uniqueness of the correspondence between image pairs. The proposed ICC has the advantage of 1) improving registration accuracy and robustness, 2) not requiring explicitly computing the inverse of the deformation field, and 3) reducing the inverse consistency error (ICE). Moreover, a variational registration model, based on the maximum likelihood estimation, is proposed to accelerate the algorithm convergence and allow for inexact image pixel matching within an optimized variation for noisy image pairs.
Machine And Deep Learning In Oncology Medical Physics And Radiology
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Author : Issam El Naqa
language : en
Publisher: Springer Nature
Release Date : 2022-02-02
Machine And Deep Learning In Oncology Medical Physics And Radiology written by Issam El Naqa and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-02 with Science categories.
This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology. Readers will find thorough coverage of basic theory, methods, and demonstrative applications in these fields. An introductory section explains machine and deep learning, reviews learning methods, discusses performance evaluation, and examines software tools and data protection. Detailed individual sections are then devoted to the use of machine and deep learning for medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Resources for varying applications are provided in each chapter, and software code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
Machine Learning In Radiation Oncology
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Author : Issam El Naqa
language : en
Publisher: Springer
Release Date : 2015-06-19
Machine Learning In Radiation Oncology written by Issam El Naqa and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-19 with Medical categories.
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 Based Adaptive Radiotherapy Treatments From Bench Top To Bedside
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Author : Jiahan Zhang
language : en
Publisher: Frontiers Media SA
Release Date : 2023-05-12
Machine Learning Based Adaptive Radiotherapy Treatments From Bench Top To Bedside written by Jiahan Zhang and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-12 with Medical categories.
Machine Learning With Radiation Oncology Big Data
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Author : Jun Deng
language : en
Publisher: Frontiers Media SA
Release Date : 2019-01-21
Machine Learning With Radiation Oncology Big Data written by Jun Deng and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-21 with categories.
Adaptive Radiation Therapy
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Author : X. Allen Li
language : en
Publisher: CRC Press
Release Date : 2011-01-27
Adaptive Radiation Therapy written by X. Allen Li and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-01-27 with Medical categories.
Modern medical imaging and radiation therapy technologies are so complex and computer driven that it is difficult for physicians and technologists to know exactly what is happening at the point-of-care. Medical physicists responsible for filling this gap in knowledge must stay abreast of the latest advances at the intersection of medical imaging an
Machine Learning And Artificial Intelligence In Radiation Oncology
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Author : Barry S. Rosenstein
language : en
Publisher: Academic Press
Release Date : 2023-12-02
Machine Learning And Artificial Intelligence In Radiation Oncology written by Barry S. Rosenstein and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-02 with Science categories.
Machine Learning and Artificial Intelligence in Radiation Oncology: A Guide for Clinicians is designed for the application of practical concepts in machine learning to clinical radiation oncology. It addresses the existing void in a resource to educate practicing clinicians about how machine learning can be used to improve clinical and patient-centered outcomes. This book is divided into three sections: the first addresses fundamental concepts of machine learning and radiation oncology, detailing techniques applied in genomics; the second section discusses translational opportunities, such as in radiogenomics and autosegmentation; and the final section encompasses current clinical applications in clinical decision making, how to integrate AI into workflow, use cases, and cross-collaborations with industry. The book is a valuable resource for oncologists, radiologists and several members of biomedical field who need to learn more about machine learning as a support for radiation oncology. Presents content written by practicing clinicians and research scientists, allowing a healthy mix of both new clinical ideas as well as perspectives on how to translate research findings into the clinic Provides perspectives from artificial intelligence (AI) industry researchers to discuss novel theoretical approaches and possibilities on academic collaborations Brings diverse points-of-view from an international group of experts to provide more balanced viewpoints on a complex topic
Machine Learning With Radiation Oncology Big Data
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Author :
language : en
Publisher:
Release Date : 2019
Machine Learning With Radiation Oncology Big Data written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Radiation oncology is uniquely positioned to harness the power of big data as vast amounts of data are generated at an unprecedented pace for individual patients in imaging studies and radiation treatments worldwide. The big data encountered in the radiotherapy clinic may include patient demographics stored in the electronic medical record (EMR) systems, plan settings and dose volumetric information of the tumors and normal tissues generated by treatment planning systems (TPS), anatomical and functional information from diagnostic and therapeutic imaging modalities (e.g., CT, PET, MRI and kVCBCT) stored in picture archiving and communication systems (PACS), as well as the genomics, proteomics and metabolomics information derived from blood and tissue specimens. Yet, the great potential of big data in radiation oncology has not been fully exploited for the benefits of cancer patients due to a variety of technical hurdles and hardware limitations. With recent development in computer technology, there have been increasing and promising applications of machine learning algorithms involving the big data in radiation oncology. This research topic is intended to present novel technological breakthroughs and state-of-the-art developments in machine learning and data mining in radiation oncology in recent years.
Fundamentals Of Radiation Oncology
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Author : Hasan Murshed
language : en
Publisher: Elsevier
Release Date : 2024-06-28
Fundamentals Of Radiation Oncology written by Hasan Murshed and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-28 with Medical categories.
Fundamentals of Radiation Oncology: Physical, Biological, and Clinical Aspects, Fourth Edition, is written by a team of renowned experts. This book is a must-have resource for anyone practicing radiation oncology. From basic principles to more-advanced planning and delivery of radiation therapy to treat cancer, this book is a go-to resource for mastering the art and science of radiation oncology. Recent advances in SRS, SBRT, proton therapy, an immunotherapy New chapters on adaptive radiotherapy, and artificial intelligence in radiation therapy IMRT and IGRT techniques are covered in depth in all clinical chapters Latest landmark studies provide evidence-based rationale for recommended treatments Radiation treatment toxicity and its management