Computed Tomography
Algorithms, Insight, and Just Enough Theory
Published on 30. October 2021
Book
Paperback/Softback
337 pages
978-1-61197-666-3 (ISBN)
Description
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.
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.
More details
Series
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Weight
333 gr
ISBN-13
978-1-61197-666-3 (9781611976663)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Per Christian Hansen is Professor of Scientific Computing at the Department of Applied Mathematics and Computer Science, Technical University of Denmark. He is a 2015 SIAM Fellow and a 2019 Villum Investigator. He currently heads a research initiative on computational uncertainty quantification for inverse problems.
Jakob Sauer Jorgensen is a Senior Researcher at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). He became a Presidential Fellow (academic tenure track) at the University of Manchester in 2018, before returning to DTU in 2020 in a permanent research-track academic position.
William R. B. Lionheart is a Professor of Applied Mathematics at the University of Manchester. He works on analytical, numerical, and practical aspects of a wide range of imaging and inverse problems. His current work includes X-ray, electron and neutron tomography, increasingly rich tomography methods involving spectral, diffraction or polarimetric data to produce non-scalar 3D images.
Martin S. Andersen has held a postdoctoral position in the Division of Automatic Control at Linkoeping University, Sweden, and at the Technical University of Denmark, where he is currently an Associate Professor in the Department of Applied Mathematics and Computer Science. His main research interests include optimization, signal and image processing, and systems and control.
K. Joost Batenburg is Professor of Imaging and Visualization at the Leiden Institute of Advanced Computer Science (LIACS) and leads the Computational Imaging group at CWI, the national research center for mathematics and computer science in Amsterdam, The Netherlands. His current research focuses on creating a real-time tomography pipeline.
Yiqiu Dong is Associate Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark. Her research area includes mathematical modeling and algorithms in imaging sciences, inverse problems and variational methods, matrix application and computation, and optimization methods.
Eric Todd Quinto is the Robinson Professor of Mathematics at Tufts University. When he started at Tufts, he met Nobel Prize winner Allan Cormack, who introduced Quinto to tomography. Quinto specializes in analyzing strengths and issues in limited data tomography-when some tomographic data are missing.
Jan Sijbers is a full professor at the University of Antwerp, where he leads imec-Vision Lab, a research group on image reconstruction, processing and analysis. His main interests are in Magnetic Resonance Imaging and X-ray CT.
Jakob Sauer Jorgensen is a Senior Researcher at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). He became a Presidential Fellow (academic tenure track) at the University of Manchester in 2018, before returning to DTU in 2020 in a permanent research-track academic position.
William R. B. Lionheart is a Professor of Applied Mathematics at the University of Manchester. He works on analytical, numerical, and practical aspects of a wide range of imaging and inverse problems. His current work includes X-ray, electron and neutron tomography, increasingly rich tomography methods involving spectral, diffraction or polarimetric data to produce non-scalar 3D images.
Martin S. Andersen has held a postdoctoral position in the Division of Automatic Control at Linkoeping University, Sweden, and at the Technical University of Denmark, where he is currently an Associate Professor in the Department of Applied Mathematics and Computer Science. His main research interests include optimization, signal and image processing, and systems and control.
K. Joost Batenburg is Professor of Imaging and Visualization at the Leiden Institute of Advanced Computer Science (LIACS) and leads the Computational Imaging group at CWI, the national research center for mathematics and computer science in Amsterdam, The Netherlands. His current research focuses on creating a real-time tomography pipeline.
Yiqiu Dong is Associate Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark. Her research area includes mathematical modeling and algorithms in imaging sciences, inverse problems and variational methods, matrix application and computation, and optimization methods.
Eric Todd Quinto is the Robinson Professor of Mathematics at Tufts University. When he started at Tufts, he met Nobel Prize winner Allan Cormack, who introduced Quinto to tomography. Quinto specializes in analyzing strengths and issues in limited data tomography-when some tomographic data are missing.
Jan Sijbers is a full professor at the University of Antwerp, where he leads imec-Vision Lab, a research group on image reconstruction, processing and analysis. His main interests are in Magnetic Resonance Imaging and X-ray CT.