Faced with a single neuroradiological image of an unknown patient, how confident would you be to make a differential diagnosis? Despite advanced imaging techniques, a confident diagnosis also requires knowledge of the patient's age, clinical data and the lesion location. Pattern Recognition Neuroradiology provides the tools you will need to arrive at the correct diagnosis or a reasonable differential diagnosis. This user-friendly book includes basic information often omitted from other texts: a practical method of image analysis, sample dictation templates and didactic information regarding lesions/diseases in a concise outline form. Image galleries show more than 700 high quality representative examples of the diseases discussed. Whether you are a trainee encountering some of these conditions for the first time or a resident trying to develop a reliable system of image analysis, Pattern Recognition Neuroradiology is an invaluable diagnostic resource.

The explosion of insights in the field of metabolic disease has shed new light on diagnostic as well as treatment options. ‘Inherited Metabolic Disease – A Clinical Approach’ is written with a reader-friendly consistent structure. It helps the reader to find the information in an easily accessible and rapid way when needed. Starting with an overview of the major groups of metabolic disorders it includes algorithms with questions and answers as well as numerous graphs, metabolic pathways, and an expanded index. Clinical and diagnostic details with a system and symptom based are given to facilitate an efficient and yet complete diagnostic work-up of individual patients. Further, it offers helpful advice for emergency situations, such as hypoglycemia, hyperammonemia, lactic acidosis or acute encephalopathy. Five different indices allow a quick but complete orientation for common important constellations. Last but not least, it has an appendix with a guide to rapid differential diagnosis of signs and symptoms and when not to suspect metabolic disease. It will help physicians to diagnose patients they may otherwise fail to diagnose and to reduce unnecessary referrals. For metabolic and genetic specialists especially the indices will be helpful as a quick look when being called for advice. It has all it needs to become a gold standard defining the clinical practice in this field.

Part of the in-depth and practical Pattern Recognition series, Practical Surgical Neuropathology, 2nd Edition, by Drs. Arie Perry and Daniel J. Brat, helps you arrive at an accurate CNS diagnosis by using a pattern-based approach. Leading diagnosticians in neuropathology guide you from a histological (and/or clinical, radiologic, and molecular) pattern, through the appropriate work-up, around the pitfalls, and to the best diagnosis. Almost 2,000 high-quality illustrations capture key neuropathological patterns for a full range of common and rare conditions, and a "visual index" at the beginning of the book directs you to the exact location of in-depth diagnostic guidance. - Instructive algorithms provide detailed guidance based on 8 major (scanning magnification) patterns and 20 minor (high magnification) patterns – helping you narrow the range of diagnostic possibilities. - A user-friendly design color-codes patterns to specific entities, and key points are summarized in tables, charts, and graphs so you can quickly and easily find what you are looking for. Sweeping content updates. Patterns call-outs throughout. The patterns described initially in the text will be better linked directly within the chapter, reinforcing the patterns for further understanding.

Gamuts in Radiology is the world's most complete, best known, and most trusted guide to radiologic differential diagnosis. Since 1975, radiologists the world over have used it to ensure that every diagnostic possibility is considered. For the Fourth Edition, Dr. Maurice M. Reeder has assembled an all-new board of Section Editors who have completely revised and updated their respective sections. New features in the fourth edition include: over 250 new gamuts, updates in more than 80 percent of the previous gamuts, an entire new section on obestetrical ultrasound.

"Faced with a single neuroradiological image of an unknown patient, how confident would you be to make a differential diagnosis? Despite advanced imaging techniques, a confident diagnosis also requires knowledge of the patient's age, clinical data and the lesion location. Pattern Recognition Neuroradiology provides the tools you will need to arrive at the correct diagnosis or a reasonable differential diagnosis. This user-friendly book includes basic information often omitted from other texts: a practical method of image analysis, sample dictation templates and didactic information regarding lesions/diseases in a concise outline form. Image galleries show more than 700 high quality representative examples of the diseases discussed. Whether you are a trainee encountering some of these conditions for the first time or a resident trying to develop a reliable system of image analysis, Pattern Recognition Neuroradiology is an invaluable diagnostic resource"--Provided by publisher.

This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.

Magnetic resonance imaging (MRI) is now considered the imaging modality of choice for the majority of disorders affecting the central nervous system. This is particularly true for gray and white matter disorders, thanks to the superb soft tis sue contrast in MRI which allows gray matter, unmyelinated, and myelinated white matter to be distinguished and their respective disorders identified. The pre sent book is devoted to the disorders of myelin and myelination. A growing amount of detailed in vivo information about myelin, myelination, and myelin dis orders has been derived both from MRI and from MR spectroscopy (MRS). This prompted us to review the clinical, laboratory, biochemical, and pathological data on this subject in order to integrate all available information and to provide im proved insights into normal and disordered myelin and myelination. We will show how the synthesis of all available information contributes to the interpretation of MR images. After a brief historical review about the increasing knowledge on myelin and my elin disorders, we propose a new classification of myelin disorders based on the subcellular localization of the enzymatic defects as far as the inborn errors of me tabolism are concerned. This classification serves as a guide throughout the book. All items of the classification will be discussed and, whenever relevant and possi ble, be illustrated by MR images.