This book examines non-invasive, electrical-based methods for disease diagnosis and assessment of heart function. In particular, a formalized signal model is proposed since this offers several advantages over methods that rely on measured data alone. By using a formalized representation, the parameters of the signal model can be easily manipulated and/or modified, thus providing mechanisms that allow researchers to reproduce and control such signals. In addition, having such a formalized signal model makes it possible to develop computer tools that can be used for manipulating and understanding how signal changes result from various heart conditions, as well as for generating input signals for experimenting with and evaluating the performance of e.g. signal extraction methods. The work focuses on bioelectrical information, particularly electrical bio-impedance (EBI). Once the EBI has been measured, the corresponding signals have to be modelled for analysis. This requires a structured approach in order to move from real measured data to the model of the corresponding signals. This book proposes a generic framework for this procedure. It can be used as a guide for modelling impedance cardiography (ICG) and impedance respirography (IRG) signals, as well as for developing the corresponding bio-impedance signal simulator (BISS).

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.

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.

Traditional research methodologies in the human respiratory system have always been challenging due to their invasive nature. Recent advances in medical imaging and computational fluid dynamics (CFD) have accelerated this research. This book compiles and details recent advances in the modelling of the respiratory system for researchers, engineers, scientists, and health practitioners. It breaks down the complexities of this field and provides both students and scientists with an introduction and starting point to the physiology of the respiratory system, fluid dynamics and advanced CFD modeling tools. In addition to a brief introduction to the physics of the respiratory system and an overview of computational methods, the book contains best-practice guidelines for establishing high-quality computational models and simulations. Inspiration for new simulations can be gained through innovative case studies as well as hands-on practice using pre-made computational code. Last but not least, students and researchers are presented the latest biomedical research activities, and the computational visualizations will enhance their understanding of physiological functions of the respiratory system.

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.

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.

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.

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 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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"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

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.

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.

Computational methodologies of signal processing and imaging analysis, namely considering 2D and 3D images, are commonly used in different applications of the human society. For example, Computational Vision systems are progressively used for surveillance tasks, traf?c analysis, recognition process, inspection p- poses, human-machine interfaces, 3D vision and deformation analysis. One of the main characteristics of the Computational Vision domain is its int- multidisciplinary. In fact, in this domain, methodologies of several more fundam- tal sciences, such as Informatics, Mathematics, Statistics, Psychology, Mechanics and Physics are usually used. Besides this inter-multidisciplinary characteristic, one of the main reasons that contributes for the continually effort done in this domain of the human knowledge is the number of applications in the medical area. For instance, it is possible to consider the use of statistical or physical procedures on medical images in order to model the represented structures. This modeling can have different goals, for example: shape reconstruction, segmentation, registration, behavior interpretation and simulation, motion and deformation analysis, virtual reality, computer-assisted therapy or tissue characterization. The main objective of the ECCOMAS Thematic Conferences on Computational Vision and Medical Image Processing (VIPimage) is to promote a comprehensive forum for discussion on the recent advances in the related ?elds trying to id- tify widespread areas of potential collaboration between researchers of different sciences.

The aim of this book is to outline the concept of entropy, various types of entropies and their implementation to evaluate a variety of biomedical signals/images. The book emphasizes various entropy-based image pre-processing methods which are essential for the development of suitable computerized examination systems. The recent research works on biomedical signal evaluation confirms that signal analysis provides vital information regarding the physiological condition of the patient, and the efficient evaluation of these signals can help to diagnose the nature and the severity of the disease. This book emphasizes various entropy-based image pre-processing methods which are essential for the development of suitable computerized examination systems for the analysis of biomedical images recorded with a variety of modalities. The work discusses the image pro-processing methods with the Entropies, such as Kapur, Tsallis, Shannon and Fuzzy on a class of RGB-scaled and gray-scaled medical pictures. The performance of the proposed technique is justified with the help of suitable case studies, which involves x-ray image analysis, MRI analysis and CT analysis. This book is intended for medical signal/image analysts, undergraduate and postgraduate students, researchers, and medical scientists interested in biomedical data evaluation.

Advances in digital technology led to the development of digital x-ray detectors that are currently in wide use for projection radiography, including Computed Radiography (CR) and Digital Radiography (DR). Digital Imaging Systems for Plain Radiography addresses the current technological methods available to medical imaging professionals to ensure the optimization of the radiological process concerning image quality and reduction of patient exposure. Based on extensive research by the authors and reference to the current literature, the book addresses how exposure parameters influence the diagnostic quality in digital systems, what the current acceptable radiation doses are for useful diagnostic images, and at what level the dose could be reduced to maintain an accurate diagnosis. The book is a valuable resource for both students learning the field and for imaging professionals to apply to their own practice while performing radiological examinations with digital systems.

Advanced Imaging of the Abdomen is invaluable to the practising radiologist, and the more senior radiology resident and fellow, who is looking for a background reference source when discussing a suggested imaging approach with the referring physician.

The book includes extensive lists, tables, line drawings and illustrations - ultrasonography, computed tomography, magnetic resonance images, scintigraphy. It bridges the interface between the referring clinician and radiologist when faced with a patient suspected of having a complex or more unusual abdominal condition.

Images in Urology is a unique book that integrates images of urological conditions within their clinical context. Improvements in imaging techniques have meant greater diagnostic power and a dramatic rise in the number and quality of images obtained and viewed by practicing clinicians. None more so than in the field of urology, where static and dynamic images are fundamental to the diagnosis and treatment of almost all conditions. This book presents images of radiological and radionucleotide scans, macroscopic and microscopic histopathology specimens, urodynamic traces and photographs of dermatological conditions relating to urology. Each section has a series of questions, often relating to a clinical scenario, about the images. A comprehensive answer provides a description of each image and of the condition shown. Details of how to interpret the image and the use of contrast or staining methods to help differentiate normal anatomy from pathology are included. Images in Urology is an essential tool for urology, radiology and histopathology trainees and consultants, as well as being an excellent exam preparation guide.

In view of better results expected from examination of medical datasets (images) with hybrid (integration of thresholding and segmentation) image processing methods, this work focuses on implementation of possible hybrid image examination techniques for medical images. It describes various image thresholding and segmentation methods which are essential for the development of such a hybrid processing tool. Further, this book presents the essential details, such as test image preparation, implementation of a chosen thresholding operation, evaluation of threshold image, and implementation of segmentation procedure and its evaluation, supported by pertinent case studies. Aimed at researchers/graduate students in the medical image processing domain, image processing, and computer engineering, this book: Provides broad background on various image thresholding and segmentation techniques Discusses information on various assessment metrics and the confusion matrix Proposes integration of the thresholding technique with the bio-inspired algorithms Explores case studies including MRI, CT, dermoscopy, and ultrasound images Includes separate chapters on machine learning and deep learning for medical image processing

Gamma cameras are traditionally large devices that are situated in nuclear medicine departments, but recent advances in detector design have enabled the production of compact gamma cameras that allow nuclear imaging at the patient bedside and in the operating theatre. Gamma Cameras for Interventional and Intraoperative Imaging is the first book to cover this new area of imaging, and provides a unique insight into the experimental and clinical use of small field of view gamma cameras in hospitals. This book explores advances in the design and operation of compact gamma cameras and conducts a thorough review of current SFOV systems, before exploring the clinical applications of the technology. It is an essential reference for surgeons, operating theatre staff, clinical scientists (medical physicists), technologists, nuclear physicians and radiologists whose patients could benefit from this technology.

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.