Over recent years there has been a vast expansion in the variety of imaging techniques available, and developments in machine specifications continue apace. If radiologists and radiographers are to obtain optimal image quality while minimising exposure times, a good understanding of the fundamentals of the radiological science underpinning diagnostic imaging is essential.
The second edition of this well-received textbook continues to cover all technical aspects of diagnostic radiology, and remains an ideal companion during examination preparation and beyond. The content includes a review of basic science aspects of imaging, followed by a detailed explanation of radiological sciences, conventional x-ray image formation and other imaging techniques. The enormous technical advances in computed tomography, including multislice acquisition and 3D image reconstruction, digital imaging in the form of image plate and direct radiography, magnetic resonance imaging, colour flow imaging in ultrasound and positron radiopharmaceuticals in nuclear medicine, are all considered here. A chapter devoted to computers in radiology considers advances in radiology information systems and computer applications in image storage and communication systems. The text concludes with a series of general topics relating to diagnostic imaging.
The content has been revised and updated throughout to ensure it remains in line with the Fellowship of the Royal College of Radiologists (FRCR) examination, while European and American perspectives on technology, guidelines and regulations ensure international relevance.

This book provides a thorough yet concise introduction to quantitative radiobiology and radiation physics, particularly the practical and medical application. Beginning with a discussion of the basic science of radiobiology, the book explains the fast processes that initiate damage in irradiated tissue and the kinetic patterns in which such damage is expressed at the cellular level. The final section is presented in a highly practical handbook style and offers application-based discussions in radiation oncology, fractionated radiotherapy, and protracted radiation among others. The text is also supplemented by a Web site.

Segmentation of anatomical structures in medical image data is an essential task in clinical practice. Clinical applications that call for image segmentation include virtual surgery planning, therapy planning, diagnosis, and patient monitoring. This thesis contributes methods for accurate fully automatic segmentation of certain anatomical structures in 3D medical image data. It follows the Deformable Model approach for segmentation, and makes use of Statistical Shape Models.
The core methodological contribution of this thesis is a novel deformation model for triangle meshes that overcomes limitations of state-of-the-art approaches. This deformation model allows for accurate segmentation of tip- and ridge-shaped features of anatomical structures. Concerning methodology, a second focus of this work lies on accurate multi-object segmentation.
As for practical contributions, this thesis proposes application-specific segmentation pipelines for a range of anatomical structures, together with thorough evaluations of segmentation accuracy on clinical image data. These fully automatic pipelines allow for highly accurate segmentation as compared to related work. E.g., the pipeline proposed for segmentation of the liver in contrast-enhanced CT is, as at June 2014, the most accurate among all competing fully automatic approaches on benchmark image data

The process of collecting and analyzing the data is critical in healthcare as it constitutes the basis for categorization of patient health problems. Data collected in medical practice ranges from free form text to structured text, numerical measurements, recorded signals, and imaging data. When admitted to the hospital, the patient often experiences additional tests varying from simple examinations such as blood tests, x-rays and electrocardiograms (ECGs), to more complex ones such as genetic tests, electromyograms (EMGs), computed tomography (CT), magnetic resonance imaging (MRI), and position emission tomography (PET). Historically, the demographics collected from all these tests were characterized by uncertainty because often there was not a single authoritative source for patient demographic information, and multiple points of human-entered data were not all in perfect agreement. The results from these tests are then archived in databases and subsequently retrieved (or not—if the correct demographic has been forgotten) upon requests by clinicians for patient management and analysis.

Advances in automated image detection of retinal pathology require interaction and dialogue between practitioners from diverse fields. In this spirit, the contributors to this book include engineers, physicists, computer scientists, and physicians to provide a nexus between these fields. Each contributor is a recognized expert and has made significant contributions to the development of automated analysis of retinal images. There is much that has been discovered and proved to be effective in research institutes but has failed to make the transition to general clinical practice. Therefore many problems are yet to be solved.
This book is intended for researchers in the diverse fields of engineering, mathematics, and physics, as well as biologists and physicians. It discusses the epidemiology of disease, screening protocols, algorithm development, image processing, and feature analysis applied to the retina. Hopefully it inspires readers that automated analysis of retinal images is an exciting field, both for the physician and nonphysician alike, and one in which many developments have been made, but much more needs to be done if the products of our labor are to benefit the health of the community.

Since the fi rst report on OCT over two decades ago, interest in this imaging technology in medicine has exponentially increased, initially in ophthalmology and recently in cardiology. Introduction of this intravascular imaging modality in cardiology coincided with the emergence of a new concept, namely the detection of “vulnerable or high-risk plaque.” As vulnerable plaque represents microstructural changes of coronary plaque including thin fi brous cap overlying lipid pool and increased lipid pool, it was thought that this high-resolution imaging modality would be ideal for detecting vulnerable plaques. Indeed, OCT has been proven to be an invaluable tool not only for detecting high-risk plaques, but also for studying in vivo vascular biology, particularly in order to understand the pathophysiology of acute coronary syndromes. With a series of OCT studies, our understanding on the pathogenesis of acute coronary syndrome has made remarkable progress and confi rmed many fi ndings, which had been possible only in autopsy studies previously. Since OCT can be used in patients repeatedly, in vivo serial studies have been reported and these studies have provided critical information on the evolution of vascular structure over time and also the vascular response to interventions such as cholesterol lowering therapy. In addition to the application of OCT to research, OCT has been increasingly used in interventional cardiology, primarily to optimize percutaneous coronary intervention with stenting. Despite gradual adoption of this technology in cardiac catheterization laboratories, the clinical value of OCT remains undefi ned. How to maximize the information provided by OCT to optimize percutaneous procedures is not widely known. This book will provide background and practical tips for interventional cardiologists.

Beginning with modest initial attempts in roughly the 1960s, digital image processing has become a recognized field of science, as well as a broadly accepted methodology, to solve practical problems in many different kinds of human activities. The applications encompass an enormous range, starting perhaps with astronomy, geology, and physics, via medical, biological, and ecological imaging and technological exploitation, up to the initially unexpected use in humane sciences, e.g., archaeology or art history. The results obtained in the area of digital image acquisition, synthesis, processing, and analysis are impressive, though it is often not generally known that digital methods have been applied. The basic concepts and theory are, of course, common to the spectrum of applications, but some aspects are more emphasized and some less in each particular application field. This book, besides introducing general principles and methods, concentrates on applications in the field of medical imaging, which is specific for at least two features: biomedical imaging often concerns internal structures of living organisms inaccessible to standard imaging methods, and the resulting images are observed, evaluated, and classified mostly by nontechnically oriented staff.

The 17th biennial International Conference/Workshop on Information Processing in Medical Imaging (IPMI) was held June 18–22, 2001, on the campus of the University of California, Davis. Following the successful meeting in beautiful Visegrad, Hungary, this year’s conference summarized important developments in a broad range of topics regarding the acquisition, analysis, and application of information from medical images.

The ability to peer into the human body is an essential diagnostic tool in medicine and is one of the key issues in health care. Our population is aging globally; for example, over 20% of the population in Japan is already over 65 years old. Older people require many more imaging investigations than do younger ones. Cancer and heart disease are the number-one killers, approaching 40% of all deaths. Improved image quality becomes essential for effective diagnostics in these cases. Shorter examination times, shift to outpatient testing, and noninvasive imaging are rapidly needed. The challenges to contain health-care costs are enormous, and technology solutions are needed to address them.
This book addresses the state-of-the-art in hardware design in the context of medical imaging of the human body. There are new exciting opportunities in ultrasound, magnetic resonance imaging (MRI), X-ray, computed tomography (CT), and nuclear medicine (PET/SPECT). Emerging detector technologies, circuit design techniques, new materials, and innovative system approaches are explored. This book is a must for anyone serious about electronics in a health-care sector.
There are four major imaging modalities described in this book. Their effective signal positions on the electromagnetic spectrum vary from kilohertz (kHz) for ultrasound, through gigahertz (GHz) for magnetic resonance imaging (MRI), to 1018 Hz for X-ray/computed tomography (CT) and nuclear medicine, over 15 orders of magnitude variation! Despite their vastly different frequencies and principles of operation, there are numerous commonalities in signal processing of signals received by these imaging detectors, such as signal amplification, filtering, multiplexing, and analogto- digital conversion. These hardware commonalities among imaging techniques merit putting all related knowledge and know-how into one publication. I sincerely hope that this book will help improve the understanding of medical imaging electronics and stimulate further interest in the development and use of this equipment to benefit us all.

In medical image analysis, major areas such as radiotherapy, surgery planning, and quantitative diagnostics benefit from shape modeling to facilitate solutions to analysis, segmentation and reconstruction problems.
Heike Hufnagel proposes a mathematically sound statistical shape model using correspondence probabilities instead of 1-to-1 correspondences. The explicit probabilistic model is employed as shape prior in an implicit level set segmentation. Due to the particular attributes of the new model, the challenging integration of explicit and implicit representations can be done in an elegant mathematical formulation, thus combining the advantages of both explicit model and implicit segmentation. Evaluations are performed to depict the characteristics and strengths of the new model and segmentation method.
The dissertation has received the Fokusfinder award 2011 by the Innovationsstiftung Schleswig-Holstein (ISH), the Basler AG and Philips Medical Systems.