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.

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.

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.

This important volume is the first to address the use of neuroimaging in civil and criminal forensic contexts and to include discussion of prior precedents and court decisions. Equally useful for practicing psychiatrists and psychologists, it reviews both the legal and ethical consideraitons of neuroimaging.

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.

This is an interdisciplinary book that presents the applications of novel laser spectroscopy and imaging techniques for the detection of cancers recently developed by some of the world's most renown researchers. The book consists of three parts and a total of 16 chapters. Each chapter is written by leading experts who are actively seeking to develop novel spectroscopic and analytical methods for cancer detection and diagnosis.In Part I, the authors present fundamentals on optics, atoms and molecules, biophysics, cancer and machine learning. These chapters are intended for those who are not experts in the field but wish to learn about fundamentals' aspects of some of the key topics that are addressed in this book. Particular attention has been given to providing key references for those who wish to go further into the fundamental aspects of atoms and molecules, light-matter interaction, optical instrumentation, machine learning and cancer.In Part II, the authors present key applications of various laser spectroscopic methods in cancer diagnosis. They have provided recent progress in cancer diagnostics obtained by combining laser spectroscopy and machine learning for the analysis of the spectra acquired from biomedical tissues and biofluids.In Part III, the authors present chapters that discuss key developments in the applications of various laser imaging techniques for cancer detection.This is one of the few books that addresses cancer detection and diagnosis using laser spectroscopic and imaging tools with an eye on providing the reader the scientific tools, including machine learning ones.

In this issue of Clinics in Perinatology, guest editors Drs. Sangam Kanekar and Sarah Sarvis Milla bring their considerable expertise to the topic of Advances in Imaging of the Fetus and Newborn. Top experts in the field provide important imaging updates to perinatologists and neonatologists who provide care to fetal, preterm, and newborn infants, helping them optimize outcomes and support families as they make decisions about clinical care, treatment, and postnatal care of affected babies. Contains 14 practice-oriented topics including fetal MRI neuroradiology: indications, safety, and normal anatomy; neuroimaging of the premature infant; imaging of abusive head trauma in infancy; intrauterine and perinatal infections; and more. Provides in-depth clinical reviews on advances in imaging of the fetus and newborn, 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.

In this issue of Radiologic Clinics, guest editors Drs. Benjamin M. Yeh and Frank H. Miller bring their considerable expertise to the topic of Hepatobiliary Imaging. Top experts in the field offer comprehensive reviews of every major aspect of hepatobiliary imaging: biliary cancer, trauma, vascular disorders, and benign liver disease. This issue also includes articles on imaging modalities (MR, CT, ultrasound), contrast agents, and "Pearls and Pitfalls." Contains 13 practice-oriented topics including abbreviated MR liver protocols; update on MR contrast agents for liver imaging: what to use and when; biliary imaging interpretation pearls and pitfalls: CT and MRI; update on biliary cancer imaging; atypical liver malignancies and diagnostic pitfalls; and more. Provides in-depth clinical reviews on hepatobiliary imaging, 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.

In this issue of Magnetic Resonance Imaging Clinics, guest editors Drs. John Conklin and Michael Lev bring their considerable expertise to the topic of MR in the Emergency Room. Top experts in the field cover key topics such as penile and scrotal trauma, thoracic emergencies, biliary obstruction, GI/GU emergencies, abdominal and pelvic emergencies in the pregnant patient, pediatric emergencies, and more. Contains 14 relevant, practice-oriented topics including acute stroke; intracranial trauma, hemorrhage, and other non-stroke vascular emergencies; spinal emergencies; head and neck emergencies; musculoskeletal trauma and infection; and more. Provides in-depth clinical reviews on MR in the emergency room, 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.

In this issue of Neurologic Clinics, guest editor Dr. Sangam Kanekar brings his considerable expertise to the topic of Imaging of Headache. Top experts in the field cover key topics such as headache attributed to disorder of the cranium and base of the skull; role of CT and MRI in evaluation of headache due to paranasal sinus and teeth disorder; imaging of painful ophthalmologic disorders; role of MRI and CT in the evaluation of headache in pregnancy and postpartum period; assessment and imaging of pediatric and adolescent headache; and more. Contains 12 relevant, practice-oriented topics including "when to and when not to" image headache; imaging appearance of migraine and tension type headache; radiology of trigeminal and glossopharyngeal neuralgias; post-traumatic headaches and post-craniotomy syndromes; imaging of headache attributed to vascular disorder; and more. Provides in-depth clinical reviews on imaging of headache, 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.

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.