Per Christian Hansen; Jakob Sauer Jørgensen; William R. B. Lionheart; Martin S. Andersen; K. Joost Batenburg; Yiqiu Dong Society for Industrial & Applied Mathematics,U.S. (2021) Pehmeäkantinen kirja
Daniel J. Bates; Jonathan D. Hauenstein; Andrew J. Sommese; Charles W. Wampler Society for Industrial & Applied Mathematics,U.S. (2013) Pehmeäkantinen kirja
J. Bennett Johnston; Stanley Harrison; Homer A. Neal; Irwin M. Pikus; D.Van Opstal; Bruce A. McKenney Centre for Strategic&International Studies,U.S. (1993) Pehmeäkantinen kirja
In Computed Tomography: Algorithms, Insight, and Just Enough Theory, readers will learn about the fundamental computational methods used for image reconstruction in computed tomography (CT). Unique in its emphasis on the interplay of modeling, computing, and algorithm development, the book presents underlying mathematical models for motivating and deriving the basic principles of CT reconstruction methods, along with insight into their advantages, limitations, and computational aspects.
Computed Tomography: Algorithms, Insight, and Just Enough Theory:
Develops the mathematical and computational aspects of three main classes of reconstruction methods. Emphasizes the link between CT and numerical methods, which is rarely found in current literature. Describes the effects of incomplete data using both microlocal analysis and the singular value decomposition (SVD). Contains computer exercises using MATLAB that allow readers to experiment with the algorithms and make the book suitable for teaching and self-study.
This book is aimed at students, researchers, and practitioners. As a textbook, it is appropriate for the following courses: Advanced Numerical Analysis, Special Topics on Numerical Analysis, Topics on Data Science, Topics on Numerical Optimization, and Topics on Approximation Theory.
