parameters are optimized to improve the CNR of the eigenimage. Maximization of a normalized CNR of the eigenimage, along with a set of diagnostic or instrumental constraints on the pulse sequence parameters, defines a non-linear constrained optimization problem, which is solved using the fixed point approach. Simulations, phantoms, and brain images are used for evaluation of the techniques and illustration of the results throughout the thesis.

This reference/text contains the latest signal processing techniques in magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) for more efficient clinical diagnoses-providing ready-to-use algorithms for image segmentation and analysis, reconstruction and visualization, and removal of distortions and artifacts for increased detec

In 1971 Dr. Paul C. Lauterbur pioneered spatial information encoding principles that made image formation possible by using magnetic resonance signals. Now Lauterbur, "father of the MRI", and Dr. Zhi-Pei Liang have co-authored the first engineering textbook on magnetic resonance imaging. This long-awaited, definitive text will help undergraduate and graduate students of biomedical engineering, biomedical imaging scientists, radiologists, and electrical engineers gain an in-depth understanding of MRI principles. The authors use a signal processing approach to describe the fundamentals of magnetic resonance imaging. You will find a clear and rigorous discussion of these carefully selected essential topics: Mathematical fundamentals Signal generation and detection principles Signal characteristics Signal localization principles Image reconstruction techniques Image contrast mechanisms Image resolution, noise, and artifacts Fast-scan imaging Constrained reconstruction Complete with a comprehensive set of examples and homework problems, Principles of Magnetic Resonance Imaging is the must-read book to improve your knowledge of this revolutionary technique.

The popularity of magnetic resonance (MR) imaging in medicine is no mystery: it is non-invasive, it produces high quality structural and functional image data, and it is very versatile and flexible. Research into MR technology is advancing at a blistering pace, and modern engineers must keep up with the latest developments. This is only possible with a firm grounding in the basic principles of MR, and Advanced Image Processing in Magnetic Resonance Imaging solidly integrates this foundational knowledge with the latest advances in the field. Beginning with the basics of signal and image generation and reconstruction, the book covers in detail the signal processing techniques and algorithms, filtering techniques for MR images, quantitative analysis including image registration and integration of EEG and MEG techniques with MR, and MR spectroscopy techniques. The final section of the book explores functional MRI (fMRI) in detail, discussing fundamentals and advanced exploratory data analysis, Bayesian inference, and nonlinear analysis. Many of the results presented in the book are derived from the contributors' own work, imparting highly practical experience through experimental and numerical methods. Contributed by international experts at the forefront of the field, Advanced Image Processing in Magnetic Resonance Imaging is an indispensable guide for anyone interested in further advancing the technology and capabilities of MR imaging.

Although research on general multidimensional systems theory has been developing rapidly in recent years, this is the first research text to appear on the subject since the early 1980s. The text describes the current state of the art nD systems and sets out a number of open problems, and gives several different perspectives on the subject. It presents a number of different solutions to major theoretical problems as well as some interesting practical results. The book comprises of a selection of plenary and other lectures given at The First International Workshop on Multidimensional (nD) Systems (NDS-98) held in 1998 in Poland, and is written by leading world specialists in the field.

With the constant increase in applications involving image processing and multimedia procedures digital signal processing (DSP) is important for modern information engineering. One- and Multidimensional Signal Processing provides an introduction to the algorithmic basics of image and TV communication systems as well as for systems in automation and robotic applications using sensor based imaging techniques. This novel combination of both one- and multidimensional signal processing discusses the similarities between the two and aids the understanding of one theory over the other. * Presents an applications-oriented approach to image processing including TV signal processing and discusses image scanning and the use of DSP procedures or digital filters * Provides clear and comprehensive coverage of basic concepts such as spatial frequency, spatio-temporal signal processing and the spectral representation of motion and tracking of moving objects * Features examples of applications including image pick-up and display as well as still image filtering and image sequence interpolation * Introduces new design strategies for finite-impulse response (FIR) filters for image processing applciations using spatial and frequency design constraints * Includes an introduction to nonlinear image processing techniques applying edge detection operators, morphological operators and rank order filters Such a practical book will have wide-ranging appeal as a valuable resource for researchers and developers and as an ideal introductory text for senior undergraduate and postgraduate students.