Algorithms For Magnetic Resonance Imaging In Radiotherapy
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| Author | : Jens Sjölund |
| Publisher | : Linköping University Electronic Press |
| Total Pages | : 77 |
| Release | : 2018-02-20 |
| Genre | : |
| ISBN | : 9176853632 |
Radiotherapy plays an increasingly important role in cancer treatment, and medical imaging plays an increasingly important role in radiotherapy. Magnetic resonance imaging (MRI) is poised to be a major component in the development towards more effective radiotherapy treatments with fewer side effects. This thesis attempts to contribute in realizing this potential. Radiotherapy planning requires simulation of radiation transport. The necessary physical properties are typically derived from CT images, but in some cases only MR images are available. In such a case, a crude but common approach is to approximate all tissue properties as equivalent to those of water. In this thesis we propose two methods to improve upon this approximation. The first uses a machine learning approach to automatically identify bone tissue in MR. The second, which we refer to as atlas-based regression, can be used to generate a realistic, patient-specific, pseudo-CT directly from anatomical MR images. Atlas-based regression uses deformable registration to estimate a pseudo-CT of a new patient based on a database of aligned MR and CT pairs. Cancerous tissue has a different structure from normal tissue. This affects molecular diffusion, which can be measured using MRI. The prototypical diffusion encoding sequence has recently been challenged with the introduction of more general gradient waveforms. One such example is diffusional variance decomposition (DIVIDE), which allows non-invasive mapping of parameters that reflect variable cell eccentricity and density in brain tumors. To take full advantage of such more general gradient waveforms it is, however, imperative to respect the constraints imposed by the hardware while at the same time maximizing the diffusion encoding strength. In this thesis we formulate this as a constrained optimization problem that is easily adaptable to various hardware constraints. We demonstrate that, by using the optimized gradient waveforms, it is technically feasible to perform whole-brain diffusional variance decomposition at clinical MRI systems with varying performance. The last part of the thesis is devoted to estimation of diffusion MRI models from measurements. We show that, by using a machine learning framework called Gaussian processes, it is possible to perform diffusion spectrum imaging using far fewer measurements than ordinarily required. This has the potential of making diffusion spectrum imaging feasible even though the acquisition time is limited. A key property of Gaussian processes, which is a probabilistic model, is that it comes with a rigorous way of reasoning about uncertainty. This is pursued further in the last paper, in which we propose a Bayesian reinterpretation of several of the most popular models for diffusion MRI. Thanks to the Bayesian interpretation it possible to quantify the uncertainty in any property derived from these models. We expect this will be broadly useful, in particular in group analyses and in cases when the uncertainty is large.
| Author | : X. Allen Li |
| Publisher | : CRC Press |
| Total Pages | : 404 |
| Release | : 2011-01-27 |
| Genre | : Medical |
| ISBN | : 1439816352 |
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
| Author | : Gary Liney |
| Publisher | : Springer |
| Total Pages | : 211 |
| Release | : 2019-06-20 |
| Genre | : Medical |
| ISBN | : 3030144429 |
This book provides, for the first time, a unified approach to the application of MRI in radiotherapy that incorporates both a physics and a clinical perspective. Readers will find detailed information and guidance on the role of MRI in all aspects of treatment, from dose planning, with or without CT, through to response assessment. Extensive coverage is devoted to the latest technological developments and emerging options. These include hybrid MRI treatment systems, such as MRI-Linac and proton-guided systems, which are ushering in an era of real-time MRI guidance. The past decade has witnessed an unprecedented rise in the use of MRI in the radiation treatment of cancer. The development of highly conformal dose delivery techniques has led to a growing need to harness advanced imaging for patient treatment. With its flexible soft tissue contrast and ability to acquire functional information, MRI offers advantages at all stages of treatment. In documenting the state of the art in the field, this book will be of value to a wide range of professionals. The authors are international experts drawn from the scientific committee of the 2017 MR in RT symposium and the faculty of the ESTRO teaching course on imaging for physicists.
| Author | : Erik R. Ranschaert |
| Publisher | : Springer |
| Total Pages | : 369 |
| Release | : 2019-01-29 |
| Genre | : Medical |
| ISBN | : 3319948784 |
This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
| Author | : Ning Wen |
| Publisher | : Frontiers Media SA |
| Total Pages | : 170 |
| Release | : 2020-06-04 |
| Genre | : |
| ISBN | : 288963762X |
| Author | : Faiz M. Khan |
| Publisher | : Lippincott Williams & Wilkins |
| Total Pages | : 584 |
| Release | : 2014-04-03 |
| Genre | : Medical |
| ISBN | : 1469881268 |
Expand your understanding of the physics and practical clinical applications of advanced radiation therapy technologies with Khan's The Physics of Radiation Therapy, 5th edition, the book that set the standard in the field. This classic full-color text helps the entire radiation therapy team—radiation oncologists, medical physicists, dosimetrists, and radiation therapists—develop a thorough understanding of 3D conformal radiotherapy (3D-CRT), stereotactic radiosurgery (SRS), high dose-rate remote afterloaders (HDR), intensity modulated radiation therapy (IMRT), image-guided radiation therapy (IGRT), Volumetric Modulated Arc Therapy (VMAT), and proton beam therapy, as well as the physical concepts underlying treatment planning, treatment delivery, and dosimetry. In preparing this new Fifth Edition, Dr. Kahn and new co-author Dr. John Gibbons made chapter-by-chapter revisions in the light of the latest developments in the field, adding new discussions, a new chapter, and new color illustrations throughout. Now even more precise and relevant, this edition is ideal as a reference book for practitioners, a textbook for students, and a constant companion for those preparing for their board exams. Features Stay on top of the latest advances in the field with new sections and/or discussions of Image Guided Radiation Therapy (IGRT), Volumetric Modulated Arc Therapy (VMAT), and the Failure Mode Event Analysis (FMEA) approach to quality assurance. Deepen your knowledge of Stereotactic Body Radiotherapy (SBRT) through a completely new chapter that covers SBRT in greater detail. Expand your visual understanding with new full color illustrations that reflect current practice and depict new procedures. Access the authoritative information you need fast through the new companion website which features fully searchable text and an image bank for greater convenience in studying and teaching. This is the tablet version which does not include access to the supplemental content mentioned in the text.
| Author | : Issam El Naqa |
| Publisher | : Springer |
| Total Pages | : 336 |
| Release | : 2015-06-19 |
| Genre | : Medical |
| ISBN | : 3319183052 |
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 | : Elke Hattingen |
| Publisher | : Springer |
| Total Pages | : 166 |
| Release | : 2015-09-02 |
| Genre | : Medical |
| ISBN | : 3642450407 |
This book describes the basics, the challenges and the limitations of state of the art brain tumor imaging and examines in detail its impact on diagnosis and treatment monitoring. It opens with an introduction to the clinically relevant physical principles of brain imaging. Since MR methodology plays a crucial role in brain imaging, the fundamental aspects of MR spectroscopy, MR perfusion and diffusion-weighted MR methods are described, focusing on the specific demands of brain tumor imaging. The potential and the limits of new imaging methodology are carefully addressed and compared to conventional MR imaging. In the main part of the book, the most important imaging criteria for the differential diagnosis of solid and necrotic brain tumors are delineated and illustrated in examples. A closing section is devoted to the use of MR methods for the monitoring of brain tumor therapy. The book is intended for radiologists, neurologists, neurosurgeons, oncologists and other scientists in the biomedical field with an interest in neuro-oncology.
| Author | : Graeme M. Bydder |
| Publisher | : John Wiley & Sons |
| Total Pages | : 791 |
| Release | : 2012-12-19 |
| Genre | : Medical |
| ISBN | : 1118590511 |
The content of this volume has been added to eMagRes (formerly Encyclopedia of Magnetic Resonance) - the ultimate online resource for NMR and MRI. Up to now MRI could not be used clinically for imaging fine structures of bones or muscles. Since the late 1990s however, the scene has changed dramatically. In particular, Graeme Bydder and his many collaborators have demonstrated the possibility – and importance – of imaging structures in the body that were previously regarded as being “MR Invisible”. The images obtained with a variety of these newly developed methods exhibit complex contrast, resulting in a new quality of images for a wide range of new applications. This Handbook is designed to enable the radiology community to begin their assessment of how best to exploit these new capabilities. It is organised in four major sections – the first of which, after an Introduction, deals with the basic science underlying the rest of the contents of the Handbook. The second, larger, section describes the techniques which are used in recovering the short T2 and T2* data from which the images are reconstructed. The third and fourth sections present a range of applications of the methods described earlier. The third section deals with pre-clinical uses and studies, while the final section describes a range of clinical applications. It is this last section that will surely have the biggest impact on the development in the next few years as the huge promise of Short T2 and T2* Imaging will be exploited to the benefit of patients. In many instances, the authors of an article are the only research group who have published on the topic they describe. This demonstrates that this Handbook presents a range of methods and applications with a huge potential for future developments. About EMR Handbooks / eMagRes Handbooks The Encyclopedia of Magnetic Resonance (up to 2012) and eMagRes (from 2013 onward) publish a wide range of online articles on all aspects of magnetic resonance in physics, chemistry, biology and medicine. The existence of this large number of articles, written by experts in various fields, is enabling the publication of a series of EMR Handbooks / eMagRes Handbooks on specific areas of NMR and MRI. The chapters of each of these handbooks will comprise a carefully chosen selection of articles from eMagRes. In consultation with the eMagRes Editorial Board, the EMR Handbooks / eMagRes Handbooks are coherently planned in advance by specially-selected Editors, and new articles are written (together with updates of some already existing articles) to give appropriate complete coverage. The handbooks are intended to be of value and interest to research students, postdoctoral fellows and other researchers learning about the scientific area in question and undertaking relevant experiments, whether in academia or industry. Have the content of this Handbook and the complete content of eMagRes at your fingertips! Visit: www.wileyonlinelibrary.com/ref/eMagRes View other eMagRes publications here
| Author | : Jiade J. Lu |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 658 |
| Release | : 2008-11-23 |
| Genre | : Medical |
| ISBN | : 3540773851 |
Radiation Oncology: An Evidence-Based Approach (ROEBA) is a reference book designed to enable radiation oncologists, including those in training, to make diagnostic and treatment decisions on the basis of the best available scientific evidence. Ease of use is ensured by a structured, reader-friendly format that offers rapid access to evidence-based recommendations. ROEBA’s orientation is entirely practical, in that the focus is solely on diagnostic/staging and treatment issues. Detailed diagnostic and therapeutic guidelines are provided for multidisciplinary cancer management as well as radiation therapy techniques. The evidence underlying each recommendation is clearly and concisely explained, and the strength of the recommendations and evidence is systemically graded. Furthermore, diagnostic and treatment algorithms are provided for the commonly diagnosed cancers. This ground-breaking text on radiation oncology is an essential tool for physicians in their daily clinical practice.
