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Advanced Image Processing in Magnetic Resonance Imaging (Signal Processing and Communications)

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Magnetic Resonance (MR) imaging produces images of the human tissues in a noninvasive manner, revealing the structure, metabolism, and function of tissues and organs. The impact of this image technique in diagnostic radiology is impressive, due to its versatility and flexibility in joining high-quality anatomical images with functional information.

Signal and image processing play a decisive role in the exploitation of MR imaging features, allowing for the extraction of diagnostic and metabolic information from images.This book attempts to cover all updated aspects of MR image processing, ranging from new acquisition techniques to state-of-art imaging techniques. Because the textbook provides the tools necessary to understand the physical and chemical principles, and the basic signal and image processing concepts and applications, it is a valuable reference book for scientists, and an essential source for upper-level undergraduate and graduate students in these disciplines.

The book’s 18 chapters are divided into five sections. The first section focuses on MR signal and image generation and reconstruction, the basics of MR imaging, advanced reconstruction algorithms, and the parallel MRI field. In the second section, the state-of-art techniques for MR images filtering are described. In particular, the second section covers the signal and noise estimation, the inhomogeneities correction and the more advanced image filtering techniques, taking into account the peculiar features of the noise in MR images. Quantitative analysis is a key issue in MR diagnostic imaging. The third section’s topics range from image registration to integration of EEG and MEG techniques with MR imaging. Two chapters cover the cardiac image quantitative analysis issue. In the fourth section, MR spectroscopy is described, from both the signal generation and the data analysis point of view. Diffusion tensor MR imaging and MR elastography are also examined. Finally, in the last section, the functional MR image processing is described in detail. Fundamentals and advanced data analysis (as exploratory approach), bayesian inference, and nonlinear analysis are also depicted.
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Parallel Computing: Fundamentals, Applications and New Directions (Advances in Parallel Computing)
Parallel Computing: Fundamentals, Applications and New Directions (Advances in Parallel Computing)
This volume gives an overview of the state-of-the-art with respect to the development of all types of parallel computers and their application to a wide range of problem areas.

The international conference on parallel computing ParCo97 (Parallel Computing 97) was held in Bonn, Germany from 19 to 22 September 1997. The first conference in this...

Systems Thinking, Third Edition: Managing Chaos and Complexity: A Platform for Designing Business Architecture
Systems Thinking, Third Edition: Managing Chaos and Complexity: A Platform for Designing Business Architecture

This is an unconventional book for an unconventional reader. It is intended for those professionals who, in addition to their specialized knowledge, would like to get a handle on life so they may put their special text into its proper context. It speaks to those thinkers and practitioners who have come to realize that learning to be...

Mastering Machine Learning With scikit-learn
Mastering Machine Learning With scikit-learn

Apply effective learning algorithms to real-world problems using scikit-learn

About This Book

  • Design and troubleshoot machine learning systems for common tasks including regression, classification, and clustering
  • Acquaint yourself with popular machine learning algorithms, including...

Applied Deep Learning with Python: Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions
Applied Deep Learning with Python: Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions

A hands-on guide to deep learning that's filled with intuitive explanations and engaging practical examples

Key Features

  • Designed to iteratively develop the skills of Python users who don't have a data science background
  • Covers the key foundational concepts...
Cyber-Physical Systems: Advances in Design & Modelling (Studies in Systems, Decision and Control)
Cyber-Physical Systems: Advances in Design & Modelling (Studies in Systems, Decision and Control)
This book presents new findings on cyber-physical systems design and modelling approaches based on AI and data-driven techniques, identifying the key industrial challenges and the main features of design and modelling processes. To enhance the efficiency of the design process, it proposes new approaches based on the concept of digital twins....
Decomposition Methodology For Knowledge Discovery And Data Mining: Theory And Applications
Decomposition Methodology For Knowledge Discovery And Data Mining: Theory And Applications
The book presents an interesting and pleasant introduction to the topic, which can guide the reader to the knowledge of the existing decomposition methods, and to the choice of those most promising for the problems he has to face. -- Zentralblatt MATH

Data Mining is the science and technology of exploring data in
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