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| 《Medical Image Reconstruction: A Conceptu》 |
| 作者:(美)曾更生 著 |
| 出版社：高等教育出版社 |
出版日期：2009/11/1 |
| ISBN：9787040204377 |
定价： 38.00元 |
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内容推荐
Medical Image Reconstruction A Conceptual Tutorial introduces the classical and modern image reconstruction technologies, such as two-dimensional (2D) parallel-beam and fan-beam imaging, three-dimensional (3D) parallel ray, parallel plane, and cone-beam imaging. This book presents both analytical and iterative methods of these technologies and their applications in X-ray CT (computed tomography), SPECT (single photon emission computed tomography), PET (positron emission tomography),and MRI (magnetic resonance imaging). Contemporary research results in exact region-of-interest (ROI) reconstruction with truncated projections,Katsevich's cone-beam filtered backprojection algorithm, and reconstruction with highly undersampled data with/o-minimization are also included.
This book is written for engineers and researchers in the field of biomedical engineering specializing in medical imaging and image processing with image reconstruction.
目录
1 Basic Principles of Tomography
1.1 Tomography
1.2 Projection
1.3 Image Reconstruction
1.4 Backprojection
1.5 Mathematical Expressions
1.6 Worked Examples
1.7 Summary
Problems
References
2 Parallel-Beam Image Reconstruction
2.1 Fourier Transform
2.2 Central Slice Theorem
2.3 Reconstruction Algorithms
2.4 A Computer Simulation
2.5 ROI Reconstruction with Truncated Projections
2.6 Mathematical Expressions
2.7 Worked Examples
2.8 Summary
Problems
References
3 Fan-Beam Image Reconstruction
3.1 Fan-Beam Geometry and Point Spread Function
3.2 Parallel-Beam to Fan-Beam Algorithm Conversion
3.3 Short Scan
3.4 Mathematical Expressions
3.5 Worked Examples
3.6 Summary
Problems
References
4 Transmission and Emission Tomography
4.1 X-Ray Computed Tomography
4.2 Positron Emission Tomography and Single Photon Emission Computed Tomography
4.3 Attenuation Correction for Emission Tomography
4.4 Mathematical Expressions
4.5 Worked Examples
4.6 Summary
Problems
References
5 3D Image Reconstruction
5.1 Parallel Line-Integral Data
5.2 Parallel Plane-Integral Data
5.3 Cone-Beam Data
5.4 Mathematical Expressions
5.5 Worked Examples
5.6 Summary
Problems
References
6 Iterative Reconstruction
6.1 Solving a System of Linear Equations
6.2 Algebraic Reconstruction Technique
6.3 Gradient Descent Algorithms
6.4 Maximum-Likelihood Expectation-Maximization Algorithms
6.5 Ordered-Subset Expectation-Maximization Algorithm
6.6 Noise Handling
6.7 Noise Modeling as a Likelihood Function
6.8 Including Prior Knowledge
6.9 Mathematical Expressions
6.10 Reconstruction Using Highly Undersampled Data with 10 Minimization
6.11 Worked Examples
6.12 Summary
Problems
References
7 MRI Reconstruction
7.1 The \"M\"
7.2 The \"R\"
7.3 The \"T\"
7.4 Mathematical Expressions
7.5 Worked Examples
7.6 Summary
Problems
References
Index
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