Frontiers Of Medical Imaging

There has been great progress and increase in demand for medical imaging. The aim of this book is to capture all major developments in all aspects of medical imaging. As such, this book consists of three major parts: medical physics which includes 3D reconstructions, image processing and segmentation in medical imaging, and medical imaging instruments and systems. As the field is very broad and growing exponentially, this book will cover major activities with chapters prepared by leaders in the field.This book takes a balanced approach in providing coverage of all major work done in the field, and thus provides readers a clear view of the frontier activities in the field. Other books may only focus on instrumentation, physics or computer algorithms. In contrast, this book contains all components so that the readers will obtain a full picture of the field. At the same time, readers can gain some deep insights into certain special topics such as 3D reconstruction and image enhancement software systems involving MRI, ultrasound, X-ray and other medical imaging modalities.

The second volume of Frontiers in European Radiology covers two very promising techniques in diagnostic radiology, namely digital radiography and nuclear mag netic resonance imaging. Leading experts in both fields from Europe and the Unit ed States were invited to give a critical overview; digital fluoroscopy is reported on mainly by American scientists since this technique has been developed primarily in the United States, while the results of nuclear magnetic resonance imaging are pre sented by British groups currently at the forefront of research in this field. The pa pers reflect the state of the art at mid-1981, when the contributors gathered for the yearly symposium on Current Topics in Diagnostic Radiology in Berne, Switzer land. Nuclear magnetic resonance imaging, also known as spin imaging or zeugmato graphy, has produced striking progress within the past few years - even within the past few months - as described in three papers of this volume. The images generally reflect the distribution of mobile protons contained within water and fats, and pro vide remarkable discrimination between different tissues. Malignant tissue might be identified with this technique, and a wide range of disorders associated with water concentration, diffusion, and flow would be amenable to study; the measurement of blood flow could be particularly interesting.

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.

This cross-disciplinary book documents the key research challenges in the mathematical sciences and physics that could enable the economical development of novel biomedical imaging devices. It is hoped that the infusion of new insights from mathematical scientists and physicists will accelerate progress in imaging. Incorporating input from dozens of biomedical researchers who described what they perceived as key open problems of imaging that are amenable to attack by mathematical scientists and physicists, this book introduces the frontiers of biomedical imaging, especially the imaging of dynamic physiological functions, to the educated nonspecialist. Ten imaging modalities are covered, from the well-established (e.g., CAT scanning, MRI) to the more speculative (e.g., electrical and magnetic source imaging). For each modality, mathematics and physics research challenges are identified and a short list of suggested reading offered. Two additional chapters offer visions of the next generation of surgical and interventional techniques and of image processing. A final chapter provides an overview of mathematical issues that cut across the various modalities.

This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.

Topic Editor H. Peter Soyer is a shareholder of MoleMap NZ Limited and e-derm consult GmbH, and undertakes regular teledermatological reporting for both companies. He is a Medical Consultant for Canfield Scientific Inc., MetaOptima and Revenio Research Oy and also a Medical Advisor for First Derm.

This book is designed to consider the recent advancements in hospitals to diagnose various diseases accurately using AI-supported detection procedures. This work examines recent AI-supported disease detection techniques from prominent researchers and clinicians working in the medical imaging processing domain. Within this book, the integration of various AI methods, such as soft computing, machine learning, deep learning, and other related works will be presented. Real clinical images utilizing AI are incorporated. The book also includes several chapters on machine learning, convoluted neural networks, segmentation, and deep learning-assisted two-class and multi-class classification.