Now a routine tool in biomedical and life science research, live cell imaging has made major progress enabling this core biochemical, cell, and molecular biology technique to become even more powerful, versatile, and affordable. In Live Cell Imaging: Methods and Protocols, a panel of expert contributors provide a comprehensive compendium of experimental approaches to live cell imaging in the form of several overview chapters followed by representative examples and case studies covering different aspects of the most current methodology. By examining a range of state-of-the-art protocols extensively validated in complex biological studies, this volume highlights new experimental and instrumental opportunities and helps researchers to select appropriate imaging methods for their specific biological questions and measurement tasks. Written in the highly successful Methods in Molecular BiologyT series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and notes on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Live Cell Imaging: Methods and Protocols promises to contribute greatly to the further development and dissemination of this fundamentally important technology which spans across many disciplines including molecular and cell biology, chemistry, physics, optics, engineering, cell physiology, and medicine. Written for: Molecular and cellular biologists, chemists, physicists, optics specialists, engineers, cell physiologists, and medical doctors

This second edition is completely revised and improved and contains eight new chapters and six new appendixes. In addition to the theoretical background on light propagation through diffusive media, this update also provides new didactical material, including: A comprehensive statistical approach to the photon penetration depth in diffusive media. An introduction to anomalous transport. An anisotropic transport approach within the framework of diffusion theory. An introduction to the invariance properties of radiative transfer in non-absorbing media. A heuristic explanation of ballistic photon propagation. An expanded description of core Monte Carlo simulation methods. A series of new analytical solutions of the diffusion equation for new geometries. Some original solutions in the time domain of the diffusion equation in the presence of Raman and fluorescence interactions. New MATLAB (R) codes of the presented solutions. A revised and enlarged set of numerical Monte Carlo results for verification of the presented solutions. An augmented bibliography covering the field of tissue optics. Although the theoretical and computational tools provided in this book have their primary use in the field of biomedical optics, there are many other applications in which they can be used, including, for example, analysis of agricultural products, study of forest canopies or clouds, and quality control of industrial food, plastic materials, or pharmaceutical products, among many others.

Choice Recommended Title, April 2021 Bioimaging: Imaging by Light and Electromagnetics in Medicine and Biology explores new horizons in biomedical imaging and sensing technologies, from the molecular level to the human brain. It explores the most up-to-date information on new medical imaging techniques, such as the detection and imaging of cancer and brain diseases. This book also provides new tools for brain research and cognitive neurosciences based on new imaging techniques. Edited by Professor Shoogo Ueno, who has been leading the field of biomedical imaging for 40 years, it is an ideal reference book for graduate and undergraduate students and researchers in medicine and medical physics who are looking for an authoritative treatise on this expanding discipline of imaging and sensing in medicine and biology. Features: Provides step-by-step explanations of biochemical and physical principles in biomedical imaging Covers state-of-the art equipment and cutting-edge methodologies used in biomedical imaging Serves a broad spectrum of readers due to the interdisciplinary topic and approach Shoogo Ueno, Ph.D, is a professor emeritus of the University of Tokyo, Tokyo, Japan. His research interests include biomedical imaging and bioelectromagnetics, particularly in brain mapping and neuroimaging, transcranial magnetic stimulation (TMS), and magnetic resonance imaging (MRI). He was the President of the Bioelectromagnetics Society, BEMS (2003-2004) and the Chairman of the Commission K on Electromagnetics in Biology and Medicine of the International Union of Radio Science, URSI (2000-2003). He was named the IEEE Magnetics Society Distinguished Lecturer during 2010 and received the d'Arsonval Medal from the Bioelectromagnetics Society in 2010.

The critical care unit is an intense clinical environment with huge responsibilities on the professionals caring for these patients. Imaging is a key source of diagnostic information, but the conditions in which diagnostic imaging has to be performed are often extremely challenging and significantly different to imaging in the non acute setting. Imaging the ICU Patient reviews imaging procedures on the ICU in a highly practical and memorable manner. Swift and efficient clinical decision-making is rewarded on the ICU and this book serves as a practical handbook.

This book chiefly addresses the analysis and design of geosynchronous synthetic aperture radar (GEO SAR) systems, focusing on the algorithms, analysis, methods used to compensate for ionospheric influences, and validation experiments for Global Navigation Satellite Systems (GNSS). Further, it investigates special problems in the GEO SAR context, such as curved trajectories, the Earth's rotation, the 'non-stop-and-go' model, high-order Doppler parameters, temporal-variant ionospheric errors etc. These studies can also be extended to SAR with very high resolution and long integration time. Given the breadth and depth of its coverage, scientists and engineers in SAR and advanced graduate students in related areas will greatly benefit from this book.

This book explains in detail the potential value of the hybrid modalities, SPECT-CT and PET-CT, in the imaging of cardiac innervation in a wide range of conditions and diseases, including ischemic heart disease, diabetes mellitus, heart failure, amyloidosis, heart transplantation, and ventricular arrhythmias. Imaging of the brain-heart axis in neurodegenerative disease and stress and of cardiotoxicity is also discussed. The roles of the various available tracers are fully considered, and individual chapters address radiopharmaceutical development under GMP, imaging physics, and kinetic modeling software. Highly relevant background information is included on the autonomic nervous system of the heart and its pathophysiology, and in addition future perspectives are discussed. Awareness of the importance of autonomic innervation of the heart for the optimal management of cardiac patients is growing, and there is an evident need for objective measurement techniques or imaging modalities. In this context, Autonomic Innervation of the Heart will be of wide interest to clinicians, researchers, and industry.

While researchers with Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) essentially addressed questions from the whole spectrum of cardiology, oncology, and the neurosciences, it was most notably the latter that provided completely new insights into physiological and disturbed human brain function. In Molecular Imaging in the Clinical Neurosciences, experts in the field provide the reader with up-to-date information on the basic principles of molecular imaging and its major applications in the clinical neurosciences. Beginning with a section offering a comprehensive review of the methodological foundations from physics, chemistry, and mathematics including mathematical modeling, essential for meaningful data analysis, this detailed volume then continues with sections on the major biological principles and neurochemical targets relevant in current neuroimaging research and the major clinical applications from the fields of psychiatry and neurology. Written for the popular Neuromethods series, this work contains the kind of key description and implementation advice that guarantees successful results. Authoritative and cutting-edge, Molecular Imaging in the Clinical Neurosciences serves as a helpful source of knowledge for both basic and clinical scientists from psychology, psychiatry, neurology, nuclear medicine, nuclear chemistry, and the associated disciplines, all of which makes molecular imaging such a rewarding, interdisciplinary field of work.

Stochastic Modeling for Medical Image Analysis provides a brief introduction to medical imaging, stochastic modeling, and model-guided image analysis. Today, image-guided computer-assisted diagnostics (CAD) faces two basic challenging problems. The first is the computationally feasible and accurate modeling of images from different modalities to obtain clinically useful information. The second is the accurate and fast inferring of meaningful and clinically valid CAD decisions and/or predictions on the basis of model-guided image analysis. To help address this, this book details original stochastic appearance and shape models with computationally feasible and efficient learning techniques for improving the performance of object detection, segmentation, alignment, and analysis in a number of important CAD applications. The book demonstrates accurate descriptions of visual appearances and shapes of the goal objects and their background to help solve a number of important and challenging CAD problems. The models focus on the first-order marginals of pixel/voxel-wise signals and second- or higher-order Markov-Gibbs random fields of these signals and/or labels of regions supporting the goal objects in the lattice. This valuable resource presents the latest state of the art in stochastic modeling for medical image analysis while incorporating fully tested experimental results throughout.

Most books discuss general and broad topics regarding molecular imagings. However, Ultrasmall Lanthanide Oxide Nanoparticles for Biomedical Imaging and Therapy, will mainly focus on lanthanide oxide nanoparticles for molecular imaging and therapeutics. Multi-modal imaging capabilities will discussed, along with up-converting FI by using lanthanide oxide nanoparticles. The synthesis will cover polyol synthesis of lanthanide oxide nanoparticles, Surface coatings with biocompatible and hydrophilic ligands will be discussed and TEM images and dynamic light scattering (DLS) patterns will be provided. Various techniques which are generally used in analyzing the synthesized surface coated nanoparticles will be explored and this section will also cover FT , IR analysis, XRD analysis, SQUID analysis, cytotoxicity measurements and proton relaxivity measurements. In vivo MR images, CT images, fluorescence images will be provided and Therapeutic application of gadolinium oxide nanoparticles will be discussed. Finally, future perpectives will be discussed. That is, present status and future works needed for clinical applications of lanthanide oxide nanoparticles to molecular imagings will be discussed.

The purpose of this monograph is both to introduce and review developed tomograhic methods for discovering 2D and 3D structures of the ionosphere and to discuss the experimental implementation of these methods. The theoretical part deals with the solution of the inverse problem of diffraction tomography for a wide range of properties of ionospheric media. Examples are given to illustrate the experimental reconstruction of electron-density distributions in ionospheric sections. In addition to addressing the specialist researcher, the detailed derivations and explanations make this book an excellent starting point for nonspecialists and graduate students who wish to enter this exciting new field to which the authors have made pioneering contributions.

Podrid's Real-World ECGs: A Master's Approach to the Art and Practice of Clinical ECG Interpretation Podrid's Real-World ECGs combines traditional case-based workbooks with a versatile Web-based program to offer students, health care professionals, and physicians an indispensable resource for developing and honing the technical skills and systematic approach needed to interpret ECGs with confidence. ECGs from real patient cases offer a complete and in-depth learning experience by focusing on fundamental electrophysiologic properties and clinical concepts as well as detailed discussion of important diagnostic findings and relevant management decisions. Six comprehensive volumes encompass more than 600 individual case studies--plus an online repository of hundreds more interactive case studies (www.realworldECGs.com)--that include feedback and discussion about the important waveforms and clinical decision-making involved. From an introductory volume that outlines the approaches and tools utilized in the analysis of all ECGs to subsequent volumes covering particular disease entities for which the ECG is useful, readers will take away the in-depth knowledge needed to successfully interpret the spectrum of routine to challenging ECGs they will encounter in their own clinical practice. Volume 2, Myocardial Abnormalities, breaks down the essential skills necessary for diagnosing acute myocardial ischemia as well as acute and chronic myocardial infarction--arguably the most important component of the ECG armamentarium across the spectrum of health care professions. It also demonstrates the skills needed for the diagnosis of myocardial hypertrophy, atrial abnormality, and pericarditis.

This book covers the main mathematical and numerical models in computational electrocardiology, ranging from microscopic membrane models of cardiac ionic channels to macroscopic bidomain, monodomain, eikonal models and cardiac source representations. These advanced multiscale and nonlinear models describe the cardiac bioelectrical activity from the cell level to the body surface and are employed in both the direct and inverse problems of electrocardiology. The book also covers advanced numerical techniques needed to efficiently carry out large-scale cardiac simulations, including time and space discretizations, decoupling and operator splitting techniques, parallel finite element solvers. These techniques are employed in 3D cardiac simulations illustrating the excitation mechanisms, the anisotropic effects on excitation and repolarization wavefronts, the morphology of electrograms in normal and pathological tissue and some reentry phenomena. The overall aim of the book is to present rigorously the mathematical and numerical foundations of computational electrocardiology, illustrating the current research developments in this fast-growing field lying at the intersection of mathematical physiology, bioengineering and computational biomedicine. This book is addressed to graduate student and researchers in the field of applied mathematics, scientific computing, bioengineering, electrophysiology and cardiology.

For over 50 years, coronary angiography has been the mainstay of diagnosing and quantifying the extent of coronary artery disease. However appreciation of the inherent limitations of conventional angiography has led to a plethora of new imaging modalities, each with their relative strengths and potential pitfalls. Advances in these techniques have given clinicians, as well as researchers, an overwhelming amount of information and the need for thoughtful interpretation. This outstanding book provides a comprehensive overview of the current status of imaging of coronary atherosclerosis. Covering the wide variety of available imaging modalities, the book provides state-of-the-art knowledge from leading authorities in each area. In their discussion, the authors include both invasive and non-invasive modalities, including the gold standard coronary angiography, the growing field of IVUS, and novel techniques such as functional imaging, molecular imaging, and the integration of biomarkers. The key concepts and practical information given in this volume will provide the reader with the necessary understanding to choosing appropriate imaging studies and building confidence in their skill set with each.

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

There is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients. The main focus of this book is to review and summarize state-of-the-art big data and deep learning approaches to analyze and integrate multiple data types for the creation of a decision matrix to aid clinicians in the early diagnosis and identification of high risk patients for human diseases and disorders. Leading researchers will contribute original research book chapters analyzing efforts to solve these important problems.

Covers different modalities for improvement of healthcare system Describes implementation strategies and their applications in diagnosis of modalities Reviews automatic identification of related disorders using medical modality Discusses bio-potential signals and their appropriate analysis for studying different disorders Includes case studies, real-time examples and research directions

This book examines the use of biomedical signal processing-EEG, EMG, and ECG-in analyzing and diagnosing various medical conditions, particularly diseases related to the heart and brain. In combination with machine learning tools and other optimization methods, the analysis of biomedical signals greatly benefits the healthcare sector by improving patient outcomes through early, reliable detection. The discussion of these modalities promotes better understanding, analysis, and application of biomedical signal processing for specific diseases. The major highlights of Biomedical Signal Processing for Healthcare Applications include biomedical signals, acquisition of signals, pre-processing and analysis, post-processing and classification of the signals, and application of analysis and classification for the diagnosis of brain- and heart-related diseases. Emphasis is given to brain and heart signals because incomplete interpretations are made by physicians of these aspects in several situations, and these partial interpretations lead to major complications. FEATURES Examines modeling and acquisition of biomedical signals of different disorders Discusses CAD-based analysis of diagnosis useful for healthcare Includes all important modalities of biomedical signals, such as EEG, EMG, MEG, ECG, and PCG Includes case studies and research directions, including novel approaches used in advanced healthcare systems This book can be used by a wide range of users, including students, research scholars, faculty, and practitioners in the field of biomedical engineering and medical image analysis and diagnosis.

This book explores novel methods for implementing X-ray diffraction technology as an imaging modality, which have been made possible through recent breakthroughs in detector technology, computational power, and data processing algorithms. The ability to perform fast, spatially-resolved X-ray diffraction throughout the volume of a sample opens up entirely new possibilities in areas such as material analysis, cancer diagnosis, and explosive detection, thus offering the potential to revolutionize the fields of medical, security, and industrial imaging and detection. Featuring chapters written by an international selection of authors from both academia and industry, the book provides a comprehensive discussion of the underlying physics, architectures, and applications of X-ray diffraction imaging that is accessible and relevant to neophytes and experts alike. Teaches novel methods for X-ray diffraction imaging Comprehensive and self-contained discussion of the relevant physics, imaging techniques, system components, and data processing algorithms Features state-of-the-art work of international authors from both academia and industry. Includes practical applications in the medical, industrial, and security sectors

In the diagnosis and treatment of movement disorders, the use of neuroimaging has expanded widely and has been an exciting, important modality for unlocking the causes of abnormal motor control. With ever improving machinery, data collection techniques and analysis methods, researchers are now being presented with an exponentially increasing amount of data that they must wade through and interpret in the context of existing knowledge about movement disorders.

In "Neuroimaging in Movement Disorders, " the editors have produced a gold-standard resource that brings together an impressive international group of authorities in their respective fields to outline the current state of knowledge. Controversies, such as conflicting findings and methodological limitations, are covered and provide the reader with a comprehensive yet pragmatic understanding of the state of science. The chapters offer both comprehensive reviews of various neuroimaging methods and also more in-depth summaries of the contributions made by neuroimaging in individual movement disorders. Although many of the neuroimaging methods that are discussed have not been routinely used in clinical practice, the authors skillfully provide the reader with adequate detail to understand the requirements for using these methods and in some cases even the starting knowledge to begin local implementation. "Neuroimaging in Movement Disorders" is an indispensable reference that will be of value to all physicians and researchers involved in the care of patients with movement disorders.

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Reflecting recent major advances in the field of artificial intelligence, Developing the Digital Lung, From First Lung CT to Clinical AI, by Dr. John Newell, is your go-to reference for all aspects of applied artificial intelligence in lung disease development, including application to clinical medicine. It provides a unique overview of the field, beginning with a review of the origins of artificial intelligence in the mid-1970s and progressing to its application to clinical medicine in the early 2020s. Organized based on the four stages of development, this practical, easy-to-use resource helps you effectively apply artificial intelligences to lung imaging. Traces the development of precise quantitative CT of diffuse lung disease through the use of applied AI, leading to faster effective diagnosis of patients with lung disease. Reviews CT manufacturers, models and scanning protocol used to produce the 3D digital maps of the lungs. Discusses how the data processed by AI algorithms can produce measures of emphysema, air trapping, and airway wall thickening in subjects with COPD and measures of pulmonary fibrosis and traction bronchiectasis in idiopathic pulmonary fibrosis (IPF). Demonstrates the differences between reactive machine AI and limited memory AI methods. Includes comprehensive case studies and current information on cloud computing. An eBook version is included with purchase. The eBook allows you to access all of the text, figures and references, with the ability to search, customize your content, make notes and highlights, and have content read aloud.

Computer-aided design (CAD) plays a key role in improving biomedical systems for various applications. It also helps in the detection, identification, predication, analysis, and classification of diseases, in the management of chronic conditions, and in the delivery of health services. This book discusses the uses of CAD to solve real-world problems and challenges in biomedical systems with the help of appropriate case studies and research simulation results. Aiming to overcome the gap between CAD and biomedical science, it describes behaviors, concepts, fundamentals, principles, case studies, and future directions for research, including the automatic identification of related disorders using CAD. Features: Proposes CAD for the study of biomedical signals to understand physiology and to improve healthcare systems' ability to diagnose and identify health disorders. Presents concepts of CAD for biomedical modalities in different disorders. Discusses design and simulation examples, issues, and challenges. Illustrates bio-potential signals and their appropriate use in studying different disorders. Includes case studies, practical examples, and research directions. Computer-Aided Design and Diagnosis Methods for Biometrical Applications is aimed at researchers, graduate students in biomedical engineering, image processing, biomedical technology, medical imaging, and health informatics.

Molecular imaging of drugs or drug carriers is a valuable tool that can provide important information on spatiotemporal distribution of drugs, allowing improved drug distribution at target sites. Chemically labelled drugs can be used to both diagnose and treat diseases. This book introduces the topic of image guided drug delivery and covers the latest imaging techniques and developments in theranostics, highlighting the interdisciplinary nature of this field as well as its translational ability. These technologies and techniques hold potential for individualised, safer therapies. The book introduces the chemistry behind labelling drugs or drug carriers for imaging. It then discusses current scientific progress in the discovery and development of theranostic agents as well as the latest advances in triggered drug delivery. Novel imaging techniques that can be combined with therapeutics are presented, as well as results and findings from early clinical trials. This text will provide postgraduates and researchers in various disciplines associated with drug discovery, including chemistry, device engineering, oncology, neurology, cardiology, imaging, and nanoscience, an overview of this important field where several disciplines have been combined to improve treatments. Readers will be introduced to techniques that can be translated to the clinic and be applied widely.

"This book presents the technology evaluation methodology from the point of view of radiological physics and contrasts the purely physical evaluation of image quality with the determination of diagnostic outcome through the study of observer performance. The reader is taken through the arguments with concrete examples illustrated by code in R, an open source statistical language." - from the Foreword by Prof. Harold L. Kundel, Department of Radiology, Perelman School of Medicine, University of Pennsylvania "This book will benefit individuals interested in observer performance evaluations in diagnostic medical imaging and provide additional insights to those that have worked in the field for many years." - Prof. Gary T. Barnes, Department of Radiology, University of Alabama at Birmingham This book provides a complete introductory overview of this growing field and its applications in medical imaging, utilizing worked examples and exercises to demystify statistics for readers of any background. It includes a tutorial on the use of the open source, widely used R software, as well as basic statistical background, before addressing localization tasks common in medical imaging. The coverage includes a discussion of study design basics and the use of the techniques in imaging system optimization, memory effects in clinical interpretations, predictions of clinical task performance, alternatives to ROC analysis, and non-medical applications. Dev P. Chakraborty, PhD, is a clinical diagnostic imaging physicist, certified by the American Board of Radiology in Diagnostic Radiological Physics and Medical Nuclear Physics. He has held faculty positions at the University of Alabama at Birmingham, University of Pennsylvania, and most recently at the University of Pittsburgh.

This book provides an accessible yet rigorous introduction to topology and homology focused on the simplicial space. It presents a compact pipeline from the foundations of topology to biomedical applications. It will be of interest to medical physicists, computer scientists, and engineers, as well as undergraduate and graduate students interested in this topic. Features: Presents a practical guide to algebraic topology as well as persistence homology Contains application examples in the field of biomedicine, including the analysis of histological images and point cloud data