Mri For Radiotherapy

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.

This book concisely reviews important advances in radiation oncology, providing practicing radiation oncologists with a fundamental understanding of each topic and an appreciation of its significance for the future of radiation oncology. It explores in detail the impact of newer imaging modalities, such as multiparametric magnetic resonance imaging (MRI) and positron emission tomography (PET) using fluorodeoxyglucose (FDG) and other novel agents, which deliver improved visualization of the physiologic and phenotypic features of a given cancer, helping oncologists to provide more targeted radiotherapy and assess the response. Due consideration is also given to how advanced technologies for radiation therapy delivery have created new treatment options for patients with localized and metastatic disease, highlighting the increasingly important role of image-guided radiotherapy in treating systemic and oligometastatic disease. Further topics include the potential value of radiotherapy in enhancing immunotherapy thanks to the broader immune-stimulatory effects, how cancer stem cells and the tumor microenvironment influence response, and the application of mathematical and systems biology methods to radiotherapy.

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

This book, edited by leading experts in radiology, nuclear medicine, and radiation oncology, offers a wide-ranging, state of the art overview of the specifics and the benefits of a multidisciplinary approach to the use of imaging in image-guided radiation treatments for different tumor types. The entire spectrum of the most important cancers treated by radiation are covered, including CNS, head and neck, lung, breast, gastrointestinal, genitourinary, and gynecological tumors. The opening sections of the book address background issues and a range of important technical aspects. Detailed information is then provided on the use of different imaging techniques for T staging and target volume delineation, response assessment, and follow-up in various parts of the body. The focus of the book ensures that it will be of interest for a multidisciplinary forum of readers comprising radiation oncologists, nuclear medicine physicians, radiologists and other medical professionals.

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.