Presented in a concise and readable format, Pediatric Radiology provides a comprehensive review of 120 pathologies commonly encountered by practicing radiologists and residents. As part of the Rotations in Radiology series, this volume offers a guided approach to imaging diagnosis with a constant depth of coverage, a structured template, and incorporation of applied physics, distinguishing it from other texts in the field. A definition is given for each pathology in this volume, followed by: demographics, clinical presentation, imaging modalities and features, imaging algorithm, applied physics, differential diagnoses and pitfalls, and a bulleted summary of key points. Designed for point-of-care use while training on a specific rotation, as well as for exam review and ongoing reference, Pediatric Radiology is the perfect tool to impart to residents, as well as to refresh for practitioners, the essential facts of common pathologies and the various modalities used to interpret them.

The Comatose Patient, Second Edition, is a critical historical overview of the concepts of consciousness and unconsciousness, covering all aspects of coma within 100 detailed case vignettes. This comprehensive text includes principles of neurologic examination of comatose patients as well as instruction of the FOUR Score coma scale, and also discusses landmark legal cases and ethical problems. As the Chair of Division of Critical Care Neurology at Mayo Clinic, Dr. Wijdicks uses his extensive knowledge to discuss a new practical multistep approach to the diagnosis of the comatose patient. Additionally, this edition includes extensive coverage of the interpretation of neuroimaging and its role in daily practice and decision making, as well as management in the emergency room and ICU. Dr. Wijdicks details long-term supportive care and an appropriate approach to communication with family members about end-of-life decision making.

To succeed in radiology, you not only need to be able to interpret diagnostic images accurately and efficiently; you also need to make wise decisions about managing your practice at every level. Whether you work in a private, group, hospital, and/or university setting, this practical resource delivers the real-world advice you need to effectively navigate day-to-day financial decisions, equipment and computer systems choices, and interactions with your partners and staff. Equips you to make the best possible decisions on assessing your equipment needs * dealing with manufacturers * purchasing versus leasing * and anticipating maintenance costs and depreciation. Helps you to identify your most appropriate options for picture archiving systems and radiology information systems * security issues * high-speed lines * storage issues * workstation assessments * and paperless filmless flow. Offers advice on dealing with departments/clinicians who wish to perform radiological procedures and provides strategies for win-win compromises, drawing the line, inpatient-versus-outpatient considerations, cost and revenue sharing, and more.

Medical Image Analysis presents practical knowledge on medical image computing and analysis as written by top educators and experts. This text is a modern, practical, self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains. Sections cover core representations and properties of digital images and image enhancement techniques, advanced image computing methods (including segmentation, registration, motion and shape analysis), machine learning, how medical image computing (MIC) is used in clinical and medical research, and how to identify alternative strategies and employ software tools to solve typical problems in MIC.

Imaging Neuroinflammation provides an overview of the molecular and cellular basis of inflammation and its effects on neuroanatomy, reviews state-of-the-art imaging tools available to measure neuroinflammation, and describes the application of those tools to both preclinical animal disease models and human disease.This book is an authoritative reference on imaging neuroinflammation, MRI, neuroinflammation, MR Spectroscopy of inflammation, Iron imaging in inflammation, and more.

Magnetic Resonance Image Reconstruction: Theory, Methods and Applications presents the fundamental concepts of MR image reconstruction, including its formulation as an inverse problem, as well as the most common models and optimization methods for reconstructing MR images. The book discusses approaches for specific applications such as non-Cartesian imaging, under sampled reconstruction, motion correction, dynamic imaging and quantitative MRI. This unique resource is suitable for physicists, engineers, technologists and clinicians with an interest in medical image reconstruction and MRI.

Breast MRI: State of the Art and Future Directions provides a comprehensive overview of the current applications of breast MRI, including abbreviated MRI, as well as presenting technical recommendations, practical implementation and associated challenges in clinical routine. In addition, the book introduces novel MRI techniques, multimodality imaging, and advanced image processing coupled with AI, reviewing their potential for impeding and future clinical implementation. This book is a complete reference on state-of-the-art breast MRI methods suitable for MRI researchers, radiographers and clinicians. Breast cancer is one of the leading causes of death among women with early detection being the key to improved prognosis and survival. Magnetic resonance imaging (MRI) of the breast is undisputedly the most sensitive imaging method to detect cancer, with a higher detection rate than mammography, digital breast tomosynthesis, and ultrasound.

Motion Correction in MR: Correction of Position, Motion, and Dynamic Changes, Volume Eight provides a comprehensive survey of the state-of-the-art in motion detection and correction in magnetic resonance imaging and magnetic resonance spectroscopy. The book describes the problem of correctly and consistently identifying and positioning the organ of interest and tracking it throughout the scan. The basic principles of how image artefacts arise because of position changes during scanning are described, along with retrospective and prospective techniques for eliminating these artefacts, including classical approaches and methods using machine learning. Internal navigator-based approaches as well as external systems for estimating motion are also presented, along with practical applications in each organ system and each MR modality covered. This book provides a technical basis for physicists and engineers to develop motion correction methods, giving guidance to technologists and radiologists for incorporating these methods in patient examinations.

Magnetic Resonance Imaging: Recording, Reconstruction and Assessment gives a detailed overview of magnetic resonance imaging (MRI), along with its applications and challenges. The book explores the abnormalities in internal human organs using MRI techniques while also featuring case studies that illustrate measures used. In addition, it explores precautionary measures used during MRI based imaging, the selection of appropriate contrast agents, and the selection of the appropriate modality during the image registration. Sections introduce medical imaging, the use of MRI in brain, cardiac, lung and kidney detection, and also discuss both 2D and 3D imaging techniques and various MRI modalities. This volume will be of interest to researchers, engineers and medical professionals involved in the development and use of MRI systems.

Targeted Cancer Imaging: Design and Synthesis of Nanoplatforms based on Tumour Biology reviews and categorizes imaging and targeting approaches according to cancer type, highlighting new and safe approaches that involve membrane-coated nanoparticles, tumor cell-derived extracellular vesicles, circulating tumor cells, cell-free DNAs, and cancer stem cells, all with the goal of pointing the way to developing precise targeting and multifunctional nanotechnology-based imaging probes in the future. This book is highly multidisciplinary, bridging the knowledge gap between tumor biology, nanotechnology, and diagnostic imaging, and thus making it suitable for researchers ranging from oncology to bioengineering. Although considerable efforts have been conducted to diagnose, improve and treat cancer in the past few decades, existing therapeutic options are insufficient, as mortality and morbidity rates remain high. One of the best hopes for substantial improvement lies in early detection. Recent advances in nanotechnology are expected to increase our current understanding of tumor biology, allowing nanomaterials to be used for targeting and imaging both in vitro and in vivo experimental models.

The book comprises three parts. The first part provides the state-of-the-art of robots for endoscopy (endorobots), including devices already available in the market and those that are still at the R&D stage. The second part focusses on the engineering design; it includes the use of polymers for soft robotics, comparing their advantages and limitations with those of their more rigid counterparts. The third part includes the project management of a multidisciplinary team, the health cost of current technology, and how a cost-effective device can have a substantial impact on the market. It also includes information on data governance, ethical and legal frameworks, and all steps needed to make this new technology available.

The last decade has seen enormous upheaval within all aspects of health care, and the gastrointestinal/ gastroenterology (GI) service has been no exception. Increasing demand for new and established diagnostic and interventional procedures has encouraged innovative models of service delivery, resulting in a range of health professionals crossing traditional practice boundaries. In particular, nurses and radiographers have seized the opportunity to develop their scope of practice, managing a range of procedures including colonoscopy, barium studies and CT colonography. This has, in many cases, freed both radiologists and gastroenterologists to take on new roles in the interventional service. The development of new procedures and new ways of working has promoted a renewed enthusiasm for critical evaluation of the GI service as a whole. As practitioners and clinicians learn new skills and extend their scope of practice it is essential that they have a thorough understanding of the basis of safe, effective and evidence based practice. This book offers the reader a detailed overview of the range of imaging procedures that may be employed in the investigation of gastrointestinal tract pathology, and explores current practice related to the subsequent patient care pathways and treatment options. It has been designed as a detailed reference guide for all health professionals who have a direct involvement in the GI tract and its imaging, including those who may be referring patients for GI radiological investigation, those professionals who are performing and subsequently reporting the procedures, and the clinicians responsible for the subsequent patient management. This book offers a unique insight into the rapidly changing radiology service, and offers introductory chapters which provide the fundamental underpinning knowledge required for safe and effective GI practice. Subsequent chapters discuss the evidence base related to a range of imaging procedures suitable for investigation of upper and lower GI symptoms, supported by key pathology chapters. The book also explores the range of treatments available for the more common GI tract pathology. Multi professional authorship. Detailed evidence-informed explanations of a range of individual GI procedures, including suggestions for problem solving and adaptation of technique. With extensive illustrations, medical images, boxes and tables. References and further reading. /ul>

Advanced Neuro MR Techniques and Applications gives detailed knowledge of emerging neuro MR techniques and their specific clinical and neuroscience applications, showing their pros and cons over conventional and currently available advanced techniques. The book identifies the best available data acquisition, processing, reconstruction and analysis strategies and methods that can be utilized in clinical and neuroscience research. It is an ideal reference for MR scientists and engineers who develop MR technologies and/or support clinical and neuroscience research and for high-end users who utilize neuro MR techniques in their research, including clinicians, neuroscientists and psychologists. Trainees such as postdoctoral fellows, PhD and MD/PhD students, residents and fellows using or considering the use of neuro MR technologies will also be interested in this book.

Deep Learning for Chest Radiographs enumerates different strategies implemented by the authors for designing an efficient convolution neural network-based computer-aided classification (CAC) system for binary classification of chest radiographs into "Normal" and "Pneumonia." Pneumonia is an infectious disease mostly caused by a bacteria or a virus. The prime targets of this infectious disease are children below the age of 5 and adults above the age of 65, mostly due to their poor immunity and lower rates of recovery. Globally, pneumonia has prevalent footprints and kills more children as compared to any other immunity-based disease, causing up to 15% of child deaths per year, especially in developing countries. Out of all the available imaging modalities, such as computed tomography, radiography or X-ray, magnetic resonance imaging, ultrasound, and so on, chest radiographs are most widely used for differential diagnosis between Normal and Pneumonia. In the CAC system designs implemented in this book, a total of 200 chest radiograph images consisting of 100 Normal images and 100 Pneumonia images have been used. These chest radiographs are augmented using geometric transformations, such as rotation, translation, and flipping, to increase the size of the dataset for efficient training of the Convolutional Neural Networks (CNNs). A total of 12 experiments were conducted for the binary classification of chest radiographs into Normal and Pneumonia. It also includes in-depth implementation strategies of exhaustive experimentation carried out using transfer learning-based approaches with decision fusion, deep feature extraction, feature selection, feature dimensionality reduction, and machine learning-based classifiers for implementation of end-to-end CNN-based CAC system designs, lightweight CNN-based CAC system designs, and hybrid CAC system designs for chest radiographs. This book is a valuable resource for academicians, researchers, clinicians, postgraduate and graduate students in medical imaging, CAC, computer-aided diagnosis, computer science and engineering, electrical and electronics engineering, biomedical engineering, bioinformatics, bioengineering, and professionals from the IT industry.

Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies: a) cytology image analysis and b) coronavirus (COVID-19) prediction, screening, and decision-making, using publicly available datasets in their respective experiments. Of many DL models, custom Convolutional Neural Network (CNN), ResNet, InceptionNet and DenseNet are used. The results follow 'with' and 'without' transfer learning (including different optimization solutions), in addition to the use of data augmentation and ensemble networks. DL models for medical imaging are suitable for a wide range of readers starting from early career research scholars, professors/scientists to industrialists.

Ultrasound Guided Musculoskeletal Procedures in Sports Medicine: A Practical Atlas provides the support practitioners need based on practical, first-hand experience of a Sports and Exercise Medicine Physician who trained in musculoskeletal sonography. Over the years, and with much practice, the lessons learned and techniques developed are summarized with relevant pictures that guide those undertaking the procedure. As musculoskeletal ultrasound forms an important tool for physicians working in this field of medicine, this book helps physicians provide increasing expectation for patients who want a safe, guided procedure when clinically warranted. While an understanding of ultrasound imaging is essential prior to ultrasound guided procedures, there are few practical guides that provide practicing clinicians with a quick reference when faced with a procedure. This book fills that void.

Recent advancements in the technology of medical imaging, such as CT and MRI scanners, are making it possible to create more detailed 3D and 4D images. These powerful images require vast amounts of digital data to help with the diagnosis of the patient. Artificial intelligence (AI) must play a vital role in supporting with the analysis of this medical imaging data, but it will only be viable as long as healthcare professionals and AI interact to embrace deep thinking platforms such as automation in the identification of diseases in patients. AI Innovation in Medical Imaging Diagnostics is an essential reference source that examines AI applications in medical imaging that can transform hospitals to become more efficient in the management of patient treatment plans through the production of faster imaging and the reduction of radiation dosages through the PET and SPECT imaging modalities. The book also explores how data clusters from these images can be translated into small data packages that can be accessed by healthcare departments to give a real-time insight into patient care and required interventions. Featuring research on topics such as assistive healthcare, cancer detection, and machine learning, this book is ideally designed for healthcare administrators, radiologists, data analysts, computer science professionals, medical imaging specialists, diagnosticians, medical professionals, researchers, and students.

Quantitative Magnetic Resonance Imaging is a 'go-to' reference for methods and applications of quantitative magnetic resonance imaging, with specific sections on Relaxometry, Perfusion, and Diffusion. Each section will start with an explanation of the basic techniques for mapping the tissue property in question, including a description of the challenges that arise when using these basic approaches. For properties which can be measured in multiple ways, each of these basic methods will be described in separate chapters. Following the basics, a chapter in each section presents more advanced and recently proposed techniques for quantitative tissue property mapping, with a concluding chapter on clinical applications. The reader will learn: The basic physics behind tissue property mapping How to implement basic pulse sequences for the quantitative measurement of tissue properties The strengths and limitations to the basic and more rapid methods for mapping the magnetic relaxation properties T1, T2, and T2* The pros and cons for different approaches to mapping perfusion The methods of Diffusion-weighted imaging and how this approach can be used to generate diffusion tensor maps and more complex representations of diffusion How flow, magneto-electric tissue property, fat fraction, exchange, elastography, and temperature mapping are performed How fast imaging approaches including parallel imaging, compressed sensing, and Magnetic Resonance Fingerprinting can be used to accelerate or improve tissue property mapping schemes How tissue property mapping is used clinically in different organs

Computer vision and machine intelligence paradigms are prominent in the domain of medical image applications, including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics. Medical image analysis and understanding are daunting tasks owing to the massive influx of multi-modal medical image data generated during routine clinal practice. Advanced computer vision and machine intelligence approaches have been employed in recent years in the field of image processing and computer vision. However, due to the unstructured nature of medical imaging data and the volume of data produced during routine clinical processes, the applicability of these meta-heuristic algorithms remains to be investigated. Advanced Machine Vision Paradigms for Medical Image Analysis presents an overview of how medical imaging data can be analyzed to provide better diagnosis and treatment of disease. Computer vision techniques can explore texture, shape, contour and prior knowledge along with contextual information, from image sequence and 3D/4D information which helps with better human understanding. Many powerful tools have been developed through image segmentation, machine learning, pattern classification, tracking, and reconstruction to surface much needed quantitative information not easily available through the analysis of trained human specialists. The aim of the book is for medical imaging professionals to acquire and interpret the data, and for computer vision professionals to learn how to provide enhanced medical information by using computer vision techniques. The ultimate objective is to benefit patients without adding to already high healthcare costs.

SECTION 1: BASICS OF MRI 1. Basic Principles 2. T1, T2 Relaxations and Image Weighting 3. k-space and Scanning Parameters 4. Magnetic Resonance Instrumentation 5. Sequences I: Basic Principles and Classification 6. Sequences II: Accessory Techniques 7. Sequences III: When to Use What Sequences 8. Magnetic Resonance Imaging Artifacts 9. Magnetic Resonance Imaging Safety 10. Magnetic Resonance Imaging Contrast Media 11. Normal Signal Intensity on Magnetic Resonance Imaging 12. Principles of Interpretation: Neuroimaging 13. Principles of Interpretation: Body Imaging SECTION 2: MRI TECHNIQUES 14. 3 Tesla Magnetic Resonance Imaging 15. Magnetic Resonance Angiography 16. Diffusion Weighted Imaging 17. Magnetic Resonance Imaging Perfusion 18. Magnetic Resonance Spectroscopy 19. Neuroimaging Magnetic Resonance Imaging Techniques 20. Body Imaging Magnetic Resonance Imaging Techniques I 21. Body Imaging Magnetic Resonance Imaging Techniques II 22. Cardiac Magnetic Resonance Imaging Techniques 23. Lung Magnetic Resonance Imaging Techniques Index

Computational Retinal Image Analysis: Tools, Applications and Perspectives gives an overview of contemporary retinal image analysis (RIA) in the context of healthcare informatics and artificial intelligence. Specifically, it provides a history of the field, the clinical motivation for RIA, technical foundations (image acquisition modalities, instruments), computational techniques for essential operations, lesion detection (e.g. optic disc in glaucoma, microaneurysms in diabetes) and validation, as well as insights into current investigations drawing from artificial intelligence and big data. This comprehensive reference is ideal for researchers and graduate students in retinal image analysis, computational ophthalmology, artificial intelligence, biomedical engineering, health informatics, and more.

Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention.