Imaging in Movement Disorders: Imaging in Atypical Parkinsonism and Familial Movement Disorders, Volume 142, addresses the use of imaging modalities across the spectrum of movement disorders and dementias. Over the last decades, advances in neuroimaging tools have played a pivotal role in expanding our understanding of disease aetiology and pathophysiology, identifying biomarkers to monitor disease progression, aiding differential diagnosis and in the identification of novel targets for therapeutic intervention. This updated volume covers PET Molecular Imaging in Atypical Parkinsonism, SPECT Molecular Imaging in Atypical Parkinsonism, Structural MRI in Atypical Parkinsonism, Functional MRI in Atypical Parkinsonism, and more.

Imaging Methodology and Applications in Parkinson's Disease, Volume 141, provides an up-to-date and comprehensive textbook on the use of imaging modalities across the spectrum of movement disorders and dementias. Over the last decades, advances in neuroimaging tools has played a pivotal role in expanding our understanding of disease etiology and pathophysiology, identifying biomarkers to monitor disease progression, aiding differential diagnosis, and in the identification of novel targets for therapeutic intervention. This book brings together lessons learned from neuroimaging tools in movement disorders, including chapters on Advances in PET Methodology, Advances in MRI Methodology, Advances in SPECT Methodology, Hybrid PET/MRI Methodology, and more.

This issue of Surgical Oncology Clinics of North America, devoted to Imaging in Oncology, is edited by Dr. Vijay Khatri. Articles in this issue include: Imaging of Central Nervous Tumors; Role of Imaging in Head and Neck Malignancies; Imaging of Thoracic Cavity Tumor; Diagnostic Imaging of Hepatobiliary Malignancies; Recent Advances in Genito-Urinary Tract Tumors; Current Status of Imaging for Adrenal Glands; Radiology of Soft Tissue Tumors; Image-Guided Interventions in Oncology; Imaging of Pancreatic Neoplasms; Imaging of Primary Malignant Tumors of Peritoneal and Retroperitoneal Origin; Breast Tumor Imaging; and Application of Intraoperative Imaging in Oncology.

Medical imaging now plays a major role in diagnosis, choice of therapy, and follow-up. However, patients are often intimidated by the multiple imaging modalities available, the indications for their use, the imposing equipment, what the examinations are like and how long they last, and the advantages and disadvantages of various procedures. This book is designed to provide explanations for these and other issues in order to relieve some of the anxiety related to medical imaging studies.

In this issue of Neuroimaging Clinics, guest editor Dr. Tarik F. Massoud brings his considerable expertise to the topic of Neuroimaging Anatomy, Part 1: Brain and Skull. Anatomical knowledge is critical to reducing both overdiagnosis and misdiagnosis in neuroimaging. This issue is part one of a two-part series on neuroimaging anatomy that focuses on the brain, with each article addressing a specific area. The issue also includes an article on Brain Connectomics: the study of the brain's structural and functional connections between cells. Contains 13 relevant, practice-oriented topics including anatomy of cerebral cortex, lobes, and the cerebellum; brainstem anatomy; cranial nerves anatomy; brain functional imaging anatomy; imaging of normal brain aging; and more. Provides in-depth clinical reviews on neuroimaging anatomy of the brain and skull, offering actionable insights for clinical practice. Presents the latest information on this timely, focused topic under the leadership of experienced editors in the field. Authors synthesize and distill the latest research and practice guidelines to create clinically significant, topic-based reviews.

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.

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.

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.

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.

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.

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.

Before the modern age of medicine, the chance of surviving a terminal disease such as cancer was minimal at best. After embracing the age of computer-aided medical analysis technologies, however, detecting and preventing individuals from contracting a variety of life-threatening diseases has led to a greater survival percentage and increased the development of algorithmic technologies in healthcare. Deep Learning Applications in Medical Imaging is a pivotal reference source that provides vital research on the application of generating pictorial depictions of the interior of a body for medical intervention and clinical analysis. While highlighting topics such as artificial neural networks, disease prediction, and healthcare analysis, this publication explores image acquisition and pattern recognition as well as the methods of treatment and care. This book is ideally designed for diagnosticians, medical imaging specialists, healthcare professionals, physicians, medical researchers, academicians, and students.