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MR is a powerful modality. At its most advanced, it can be used not just to image anatomy and pathology, but to investigate organ function, to probe in vivo chemistry, and even to visualise the brain thinking. However, clinicians, technologists and scientists struggle with the study of the subject. The result is sometimes an obscurity of understanding, or a dilution of scientific truth, resulting in misconceptions. This is why MRI from Picture to Proton has achieved its reputation for practical clarity. MR is introduced as a tool, with coverage starting from the images, equipment and scanning protocols and traced back towards the underlying physics theory. With new content on quantitative MRI, MR safety, multi-band excitation, Dixon imaging, MR elastography and advanced pulse sequences, and with additional supportive materials available on the book's website, this new edition is completely revised and updated to reflect the best use of modern MR technology.
MRI Atlas of Human White Matter presents an atlas to the human brain on the basis of T 1-weighted imaging and diffusion tensor imaging. A general background on magnetic resonance imaging is provided, as well as the basics of diffusion tensor imaging. An overview of the principles and limitations in using this methodology in fiber tracking is included. This book describes the core white-matter structures, as well as the superficial white matter, the deep gray matter, and the cortex. It also presents a three-dimensional reconstruction and atlas of the brain white-matter tracts. The Montreal Neurological Institute coordinates, which are the most widely used, are adopted in this book as the primary coordinate system. The Talairach coordinate system is used as the secondary coordinate system. Based on magnetic resonance imaging and diffusion tensor imaging, the book offers a full segmentation of 220 white-matter and gray-matter structures with boundaries. - Visualization of brain white matter anatomy via 3D diffusion tensor imaging (DTI) contrasts and enhances relationship of anatomy to function - Full segmentation of 170+ brain regions more clearly defines structure boundaries than previous point-and-annotate anatomical labeling, and connectivity is mapped in a way not provided by traditional atlases
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This handbook describes methods for processing and analyzing functional connectivity Magnetic Resonance Imaging (fcMRI) data using the CONN toolbox, a popular freely-available functional connectivity analysis software. Content description [excerpt from introduction] The first section (fMRI minimal preprocessing pipeline) describes standard and advanced preprocessing steps in fcMRI. These steps are aimed at correcting or minimizing the influence of well-known factors affecting the quality of functional and anatomical MRI data, including effects arising from subject motion within the scanner, temporal and spatial image distortions due to the sequential nature of the scanning acquisition protocol, and inhomogeneities in the scanner magnetic field, as well as anatomical differences among subjects. Even after these conventional preprocessing steps, the measured blood-oxygen-level-dependent (BOLD) signal often still contains a considerable amount of noise from a combination of physiological effects, outliers, and residual subject-motion factors. If unaccounted for, these factors would introduce very strong and noticeable biases in all functional connectivity measures. The second section (fMRI denoising pipeline) describes standard and advanced denoising procedures in CONN that are used to characterize and remove the effect of these residual non-neural noise sources. Functional connectivity Magnetic Resonance Imaging studies attempt to quantify the level of functional integration across different brain areas. The third section (functional connectivity measures) describes a representative set of functional connectivity measures available in CONN, each focusing on different indicators of functional integration, including seed-based connectivity measures, ROI-to-ROI measures, graph theoretical approaches, network-based measures, and dynamic connectivity measures. Second-level analyses allow researchers to make inferences about properties of groups or populations, by generalizing from the observations of only a subset of subjects in a study. The fourth section (General Linear Model) describes the mathematics behind the General Linear Model (GLM), the approach used in CONN for all second-level analyses of functional connectivity measures. The description includes GLM model definition, parameter estimation, and hypothesis testing framework, as well as several practical examples and general guidelines aimed at helping researchers use this method to answer their specific research questions. The last section (cluster-level inferences) details several approaches implemented in CONN that allow researchers to make meaningful inferences from their second-level analysis results while providing appropriate family-wise error control (FWEC), whether in the context of voxel-based measures, such as when studying properties of seed-based maps across multiple subjects, or in the context of ROI-to-ROI measures, such as when studying properties of ROI-to-ROI connectivity matrices across multiple subjects.
This book highlights the latest advances in the application of artificial intelligence and data science in health care and medicine. Featuring selected papers from the 2020 Health Intelligence Workshop, held as part of the Association for the Advancement of Artificial Intelligence (AAAI) Annual Conference, it offers an overview of the issues, challenges, and opportunities in the field, along with the latest research findings. Discussing a wide range of practical applications, it makes the emerging topics of digital health and explainable AI in health care and medicine accessible to a broad readership. The availability of explainable and interpretable models is a first step toward building a culture of transparency and accountability in health care. As such, this book provides information for scientists, researchers, students, industry professionals, public health agencies, and NGOs interested in the theory and practice of computational models of public and personalized health intelligence.
A state-of-the-art review of key topics in medical image perception science and practice, including associated techniques, illustrations and examples. This second edition contains extensive updates and substantial new content. Written by key figures in the field, it covers a wide range of topics including signal detection, image interpretation and advanced image analysis (e.g. deep learning) techniques for interpretive and computational perception. It provides an overview of the key techniques of medical image perception and observer performance research, and includes examples and applications across clinical disciplines including radiology, pathology and oncology. A final chapter discusses the future prospects of medical image perception and assesses upcoming challenges and possibilities, enabling readers to identify new areas for research. Written for both newcomers to the field and experienced researchers and clinicians, this book provides a comprehensive reference for those interested in medical image perception as means to advance knowledge and improve human health.
Expanding on the National Research Council's Guide for the Care and Use of Laboratory Animals, this book deals specifically with mammals in neuroscience and behavioral research laboratories. It offers flexible guidelines for the care of these animals, and guidance on adapting these guidelines to various situations without hindering the research process. Guidelines for the Care and Use of Mammals in Neuroscience and Behavioral Research offers a more in-depth treatment of concerns specific to these disciplines than any previous guide on animal care and use. It treats on such important subjects as: The important role that the researcher and veterinarian play in developing animal protocols. Methods for assessing and ensuring an animal's well-being. General animal-care elements as they apply to neuroscience and behavioral research, and common animal welfare challenges this research can pose. The use of professional judgment and careful interpretation of regulations and guidelines to develop performance standards ensuring animal well-being and high-quality research. Guidelines for the Care and Use of Mammals in Neuroscience and Behavioral Research treats the development and evaluation of animal-use protocols as a decision-making process, not just a decision. To this end, it presents the most current, in-depth information about the best practices for animal care and use, as they pertain to the intricacies of neuroscience and behavioral research.
The concepts behind diffusion tensor imaging (DTI) are commonly difficult to grasp, even for magnetic resonance physicists. To make matters worse, a many more complex higher-order methods have been proposed over the last few years to overcome the now well-known deficiencies of DTI. In Introduction to Diffusion Tensor Imaging: And Higher Order Models, these concepts are explained through extensive use of illustrations rather than equations to help readers gain a more intuitive understanding of the inner workings of these techniques. Emphasis is placed on the interpretation of DTI images and tractography results, the design of experiments, and the types of application studies that can be undertaken. Diffusion MRI is a very active field of research, and theories and techniques are constantly evolving. To make sense of this constantly shifting landscape, there is a need for a textbook that explains the concepts behind how these techniques work in a way that is easy and intuitive to understand—Introduction to Diffusion Tensor Imaging fills this gap. - Extensive use of illustrations to explain the concepts of diffusion tensor imaging and related methods - Easy to understand, even without a background in physics - Includes sections on image interpretation, experimental design, and applications - Up-to-date information on more recent higher-order models, which are increasingly being used for clinical applications