This comprehensive book focuses on multimodality imaging technology, including overviews of the instruments and methods followed by practical case studies that highlight use in the detection and treatment of cardiovascular diseases. Chapters cover PET-CT, SPECT-CT, SPECT-MRI, PET-MRI, PET-optical imaging, SPECT-optical imaging, photoacoustic Imaging, and hybrid intravascular imaging. It also addresses the important issues of multimodality imaging probes and image quantification. Readers from radiology and cardiology as well as medical imaging and biomedical engineering will learn essentials of the field. They will be shown how the field has advanced quantitative analysis of molecularly targeted imaging through improvements in the reliability and reproducibility of imaging data. Moreover, they will be presented with quantification algorithms and case illustrations, including coverage of such topics such as multimodality image fusion and kinetic modeling. Yi-Hwa Liu, PhD is Senior Research Scientist in Cardiovascular Medicine at Yale University School of Medicine and Technical Director of Nuclear Cardiology at Yale New Haven Hospital. He is also an Associate Professor (Adjunct) of Biomedical Imaging and Radiological Sciences at National Yang-Ming University, Taipei, Taiwan, and Professor (Adjunct) of Biomedical Engineering at Chung Yuan Christian University, Taoyuan, Taiwan. He is an elected senior member of Institute of Electrical and Electronic Engineers (IEEE) and a full member of Sigma Xi of The Scientific Research Society of North America. Albert J. Sinusas, M.D., FACC, FAHA is Professor of Medicine (Section of Cardiovascular Medicine) and Radiology and Biomedical Imaging, at Yale University School of Medicine, and Director of the Yale Translational Research Imaging Center (Y-TRIC), and Director of Advanced Cardiovascular Imaging at Yale New Haven Hospital. He is a recipient of the Society of Nuclear Medicine’s Hermann Blumgart Award.

The book introduces the latest methods and algorithms developed in machine and deep learning (hybrid symbolic-numeric computations, robust statistical techniques for clustering and eliminating data as well as convolutional neural networks) dealing not only with images and the use of computers, but also their applications to visualization tasks generalized by up-to-date points of view. Associated algorithms are deposited on iCloud.

This book is devoted to the emerging field of integrated visual knowledge discovery that combines advances in artificial intelligence/machine learning and visualization/visual analytic. A long-standing challenge of artificial intelligence (AI) and machine learning (ML) is explaining models to humans, especially for live-critical applications like health care. A model explanation is fundamentally human activity, not only an algorithmic one. As current deep learning studies demonstrate, it makes the paradigm based on the visual methods critically important to address this challenge. In general, visual approaches are critical for discovering explainable high-dimensional patterns in all types in high-dimensional data offering "n-D glasses," where preserving high-dimensional data properties and relations in visualizations is a major challenge. The current progress opens a fantastic opportunity in this domain. This book is a collection of 25 extended works of over 70 scholars presented at AI and visual analytics related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level. The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations. The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes.

The recent development of three-dimensional imaging techniques has provided an enormous amount of information relevant to the clinical management of patients at low and high risk for coronary artery disease. However, while progress in each individual technique has been rapid, the correlation of findings obtained with radiology, nuclear medicine, and magnetic resonance imaging is still relatively neglected. In this book, qualified experts in cardiac imaging present the basic concepts of cardiac pathology and imaging and compare the findings obtained in particular subspecialties with those acquired using other techniques. In this way the reader will learn how images and techniques can be integrated in clinical practice to the benefit of the patient. In addition, it is explained how appropriate multimodality integration can reduce the patient's exposure to ionizing radiation. Physicians ranging from cardiac surgeons to internal medicine specialists and even public health administrators will find this book invaluable in understanding the role of hybrid cardiac imaging.

Performing any diagnostic test in medicine is always a matter of trying to get the condition of the patient diagnosed properly with the least effort, exposure, discomfort and at the same time with the lowest possible error probability. Pre-test probability is helpful but often imprecise, effectively overestimating the patient's risk profile. In a broader prevention objective, the phases of a disease, its onset, progression, and complications must be taken into account. The negative predictive value, which is so important, has in turn its main limitation in identifying the healthy patient, that is, the one who does not belong to any cluster of patients in which we would act in terms of prevention. In coronary syndromes, the goal is instead to evaluate coronary heart disease, from mild to more extensive and significant forms. For this purpose, it is necessary to use parameters that investigate different and complementary aspects: stenosis, ischemia, the morphology of the atherosclerotic plaque, metabolic processes, in particular vitality and apoptosis, the presence of inflammatory processes. The possibility, already present thanks to Hybrid Imaging, of 'joining’ exams that study different aspects, will allow the patient to be increasingly characterized not only from a diagnostic point of view but also from a prognostic and personalized therapeutic choice.

Visualization, Visual Analytics and Virtual Reality in Medicine: State-of-the-art Techniques and Applications describes important techniques and applications that show an understanding of actual user needs as well as technological possibilities. The book includes user research, for example, task and requirement analysis, visualization design and algorithmic ideas without going into the details of implementation. This reference will be suitable for researchers and students in visualization and visual analytics in medicine and healthcare, medical image analysis scientists and biomedical engineers in general. Visualization and visual analytics have become prevalent in public health and clinical medicine, medical flow visualization, multimodal medical visualization and virtual reality in medical education and rehabilitation. Relevant applications now include digital pathology, virtual anatomy and computer-assisted radiation treatment planning. - Combines visualization, virtual reality and analytics - Written by leading researchers in the field - Gives the latest state-of-the-art techniques and applications

Visual Computing for Medicine, Second Edition, offers cutting-edge visualization techniques and their applications in medical diagnosis, education, and treatment. The book includes algorithms, applications, and ideas on achieving reliability of results and clinical evaluation of the techniques covered. Preim and Botha illustrate visualization techniques from research, but also cover the information required to solve practical clinical problems. They base the book on several years of combined teaching and research experience. This new edition includes six new chapters on treatment planning, guidance and training; an updated appendix on software support for visual computing for medicine; and a new global structure that better classifies and explains the major lines of work in the field. - Complete guide to visual computing in medicine, fully revamped and updated with new developments in the field - Illustrated in full color - Includes a companion website offering additional content for professors, source code, algorithms, tutorials, videos, exercises, lessons, and more