Radiomics And Artificial Intelligence In Radiology And Nuclear Medicine

This book includes detailed explanations of the underlying technologies and concepts used in Artificial Intelligence (AI) and Machine Learning (ML) in the context of nuclear medicine and hybrid imaging. A diverse team of authors, including pioneers in the field and respected experts from leading international institutions, share their insights, opinions and outlooks on this exciting topic. A wide range of clinical applications are discussed, from brain applications to body indications, as well as the applicability of AI and ML for cardio-vascular conditions. The book also considers the potential impact of theranostics. To balance the technology-heavy and disease-specific applications, it also discusses ethical / legal issues, economic realities and the human factor, the physician. Though this discussion is not based on research and outcomes, it provides important insights into the ramifications of how AI and ML could transform Nuclear Medicine and Hybrid Imaging practice. As the first work highlighting the role of these concepts specifically in this field, rather than for medical imaging in general, this book offers a valuable resource for Nuclear Medicine Physicians, Radiologists, Physicists, Medical Imaging Administrators and Nuclear Medicine Technologists alike.

The rapid development of artificial intelligence technology in medical data analysis has led to the concept of radiomics. This book introduces the essential and latest technologies in radiomics, such as imaging segmentation, quantitative imaging feature extraction, and machine learning methods for model construction and performance evaluation, providing invaluable guidance for the researcher entering the field. It fully describes three key aspects of radiomic clinical practice: precision diagnosis, the therapeutic effect, and prognostic evaluation, which make radiomics a powerful tool in the clinical setting. This book is a very useful resource for scientists and computer engineers in machine learning and medical image analysis, scientists focusing on antineoplastic drugs, and radiologists, pathologists, oncologists, as well as surgeons wanting to understand radiomics and its potential in clinical practice. An introduction to the concepts of radiomics In-depth presentation of the core technologies and methods Summary of current radiomics research, perspective on the future of radiomics and the challenges ahead An introduction to several platforms that are planned to be built: cooperation, data sharing, software, and application platforms

Artificial Intelligence and PET Imaging, Part 1, An Issue of PET Clinics, E-Book

Nuclear Medicine and Molecular Imaging offers complete coverage of this exciting and key area of biomedicine, seamlessly fusing the science behind nuclear medicine with the clinical aspects of diagnostic imaging. Beginning with sections on the physics related to nuclear medicine, radiation biology/safety, and instrumentation/quality control, it then covers radiochemistry and the development of new radionuclide therapy/tracers, and basic techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT). The focus then shifts to the clinical aspects, with sections on clinical/preclinical nuclear imaging (structured via organ system such as gastrointestinal, cardiovascular, pulmonary, etc.), as well as other in vivo molecular imaging techniques such as optical imaging, digital image analysis (radiomics), as well as the increasing use of artificial intelligence (AI) in diagnostics. No other publication provides such a complete overview of the field, with most choosing to focus either on physics or imaging. In most cases, preclinical/experimental imaging is treated separately from clinical imaging. Nuclear Medicine and Molecular Imaging brings it all together into a complete, 4-volume, foundational resource for researchers and medical professionals specializing in this exciting area. Provides a 'one-stop' resource on all aspects of Nuclear Medicine and Molecular Imaging, including both preclinical and clinical aspects, physics and applications Presents concise, authoritative chapters expertly authored Includes high quality, full color images and videos, as well as interactive multimedia features

Choice Recommended Title, January 2021 This book, written by authors with more than a decade of experience in the design and development of artificial intelligence (AI) systems in medical imaging, will guide readers in the understanding of one of the most exciting fields today. After an introductory description of classical machine learning techniques, the fundamentals of deep learning are explained in a simple yet comprehensive manner. The book then proceeds with a historical perspective of how medical AI developed in time, detailing which applications triumphed and which failed, from the era of computer aided detection systems on to the current cutting-edge applications in deep learning today, which are starting to exhibit on-par performance with clinical experts. In the last section, the book offers a view on the complexity of the validation of artificial intelligence applications for commercial use, describing the recently introduced concept of software as a medical device, as well as good practices and relevant considerations for training and testing machine learning systems for medical use. Open problematics on the validation for public use of systems which by nature continuously evolve through new data is also explored. The book will be of interest to graduate students in medical physics, biomedical engineering and computer science, in addition to researchers and medical professionals operating in the medical imaging domain, who wish to better understand these technologies and the future of the field. Features: An accessible yet detailed overview of the field Explores a hot and growing topic Provides an interdisciplinary perspective