This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 24 full papers presented were carefully reviewed and selected from 32 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography; and deep learning for general image reconstruction.

Computerized medical imaging and image analysis have been the central focus in diagnostic radiology. They provide revolutionarizing tools for visualization of physiology as well as the understanding and quantitative measurement of physiological parameters. This book provides a unique depth of knowledge from the principles to recent advanced methods in medical imaging instrumentation and techniques as well as multidimensional image analysis and classification methods for research, education and applications in computer-aided diagnostic radiology. Internationally renowned researchers and experts in their respective areas provide detailed description of the basic foundation as well as the most recent developments in medical imaging. This book helps readers to understand theoretical and advanced concepts for important research and clinical applications.

Ionizing Radiation Detectors for Medical Imaging contains tentechnical chapters, half of which are devoted to radiology and theother half to nuclear medicine. The last chapter describes thedetectors for radiotherapy and portal imaging. Each chapter addressescompletely a specific application. The emphasis is always on detectorfundamentals and detector properties. Where necessary, software andspecific applications are described in depth. This book is intended for graduate and undergraduate students inphysics and engineering who want to study medical imaging. Inaddition, scientists who are working in a specific sub-field ofmedical imaging can acquire from the book an up-to-date description ofthe state of the art in related sub-fields, within the scope ofionizing radiation detectors. Other scientists, as well as physicians, can use the book as a reference for medical imaging

In the 25-40% of the general female population with radiodense breast parenchyma, clinically occult lesions may be invisible in the screen-film mammogram. Even if suspicious masses are detected, determination of the benign or malignant nature of a mass is often impossible from the x-ray image. There is thus a need for diagnostic procedures that can noninvasively help characterize suspicious breast lesions. Scintimammography is an imaging technique that shows promise as an adjunct diagnostic tool in problem solving mammography, for monitoring recurrence after surgery, and in the assessment of multidrug-resistance. However, because clinical Anger cameras have only moderate spatial resolution and are difficult to position close to the breast, small lesions are difficult to detect. In addition, no direct means exists of correlating mammographic and scintigraphic information because of the significantly different shape of the breast in mammography (compressed) and scintimammography (prone, pendulant). We are developing an imaging system that overcomes these problems by combining digital x-ray mammography and gamma emission scintigraphy in a single, integrated system. The system is mounted on a standard upright mammography unit, and can easily be placed in a typical mammography room, providing accessibility even for small breast imaging clinics not associated with major medical centers.

With molecular imaging becoming one the fastest growing topics in medical schools, Informa Healthcare presents Molecular Imaging in Oncology, the first comprehensive reference on molecular imaging in oncology.Giving clinicians and researchers a greater understanding of the current field, this text covers:instrumentation and techniquescancer imaging

This book constitutes the refereed proceedings of the 6th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2023, held in Conjunction with MICCAI 2023 in Vancouver, Canada, in October 2023. The book includes 16 papers which were carefully reviewed and selected from 28 full-length submissions. The 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2023) aims to bring together the top researchers in both machine learning and clinical neuroscience as well as tech-savvy clinicians to address two main challenges: 1) development of methodological approaches for analyzing complex and heterogeneous neuroimaging data (machine learning track); and 2) filling the translational gap in applying existing machine learning methods in clinical practices (clinical neuroimaging track). The papers are categorzied into topical sub-headings on Machine Learning and Clinical Applications.

Energy is a severe problem for mankind. Not only because of the population explosion, but also because human beings are calling for less poverty and better life. This work discusses the several important partnerships on a Euro-Mediterranean level.