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Introduction to the Mathematics of Medical Imaging, Second Edition
 
 
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Introduction to the Mathematics of Medical Imaging, Second Edition [Paperback]

Charles L. Epstein (Author)

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Book Description

089871642X 978-0898716429 September 28, 2007 2nd
At the heart of every medical imaging technology is a sophisticated mathematical model of the measurement process and an algorithm to reconstruct an image from the measured data. This book provides a firm foundation in the mathematical tools used to model the measurements and derive the reconstruction algorithms used in most imaging modalities in current use. In the process, it also covers many important analytic concepts and techniques used in Fourier analysis, integral equations, sampling theory, and noise analysis.

This text uses X-ray computed tomography as a "pedagogical machine" to illustrate important ideas and incorporates extensive discussions of background material making the more advanced mathematical topics accessible to readers with a less formal mathematical education. The mathematical concepts are illuminated with over 200 illustrations and numerous exercises.

New to the second edition are a chapter on magnetic resonance imaging (MRI), a revised section on the relationship between the continuum and discrete Fourier transforms, a new section on Grangreat s formula, an improved description of the gridding method, and a new section on noise analysis in MRI.

Audience The book is appropriate for one- or two-semester courses at the advanced undergraduate or beginning graduate level on the mathematical foundations of modern medical imaging technologies. The text assumes an understanding of calculus, linear algebra, and basic mathematical analysis.

Contents Preface to the Second Edition; Preface; How to Use This Book; Notational Conventions; Chapter 1: Measurements and Modeling; Chapter 2: Linear Models and Linear Equations; Chapter 3: A Basic Model for Tomography; Chapter 4: Introduction to the Fourier Transform; Chapter 5: Convolution; Chapter 6: The Radon Transform; Chapter 7: Introduction to Fourier Series; Chapter 8: Sampling; Chapter 9: Filters; Chapter 10: Implementing Shift Invariant Filters; Chapter 11: Reconstruction in X-Ray Tomography; Chapter 12: Imaging Artifacts in X-Ray Tomography; Chapter 13: Algebraic Reconstruction Techniques; Chapter 14: Magnetic Resonance Imaging; Chapter 15: Probability and Random Variables; Chapter 16: Applications of Probability; Chapter 17: Random Processes; Appendix A: Background Material; Appendix B: Basic Analysis; Index.


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Book Description

This book provides a firm foundation in the mathematical tools used to model the measurements at the heart of medical imaging technology, and now includes a new chapter on magnetic resonance imaging (MRI). Extensive background material, over 200 illustrations and numerous exercises make these advanced mathematical topics more accessible.

About the Author

Charles L. Epstein is a Professor at the University of Pennsylvania with appointments in mathematics and radiology. He works in numerical analysis, magnetic resonance imaging, and image analysis, and has also worked in a variety of fields of mathematical analysis connected to partial differential equations and several complex variables. Professor Epstein is on the editorial board of Inverse Problems and he is an editor of Birkhäuser s Cornerstones book series.

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More About the Author

Charles L. Epstein is the Thomas A. Scott Professor of Mathematics at the University of Pennsylvania in Philadelphia. His research interests include partial differential equations, medical imaging, image analysis, population genetics, and other applications of mathematical analysis to real world problems. He is the chairman of the Graduate Group in Applied Mathematics and Computational Science at Penn. His web page is http://www.math.upenn.edu/~cle

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Inside This Book (learn more)
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
imaging artifacts, random inputs, algebraic reconstruction techniques, parallel beam scanner, weak sense stationary process, spiral scan machine, uniform sample set, fan beam machine, shift invariant filter, total integral one, oscillatory artifacts, allowable events, apodizing function, fan beam scanner, exact inversion formula, finite beam width, medical image reconstruction, reconstruction grid, multiplication filter, parameter random process, fan beam data, bounded support, absolutely integrable function, periodic convolution, sense stationary random process
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Exercises Exercise, X-Ray Tomography, Exercise Exercise, Basic Definitions, Random Processes, Prove Proposition, Applications of Probability, The Euclidean, The Laplace, Isee Figure, Mathematical Modeling, Magnetic Resonance Imaging, Some Applications of Filtering Theory, Functions With Weak Derivatives, Filters Exercise, Filters Figure, Higher-Dimensional Filters, Linear Systems, Implementing Filters, Functions of Several Variables, Use Proposition, Using the Taylor, Decay of Fourier Coefficients, Limits of Random Variables, Filtering Periodic Inputs
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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