
Magnetic Resonance Image Reconstruction
Theory, Methods, and Applications
Description
Key Features
- Explains the underlying principles of MRI reconstruction, along with the latest research<
- Gives example codes for some of the methods presented
- Includes updates on the latest developments, including compressed sensing, tensor-based reconstruction and machine learning based reconstruction
Readership
Table of Contents
PART 1 Basics of MRI Reconstruction
1. Brief introduction to MRI physics
2. MRI reconstruction as an inverse problem
3. Optimization algorithms for MR reconstruction
4. Non-Cartesian MRI reconstruction
5. “Early” constrained reconstruction methodsPART 2 Reconstruction of undersampled MRI data
6. Parallel imaging
7. Simultaneous multislice reconstruction
8. Sparse reconstruction
9. Low-rank matrix and tensor–based reconstruction
10. Dictionary, structured low-rank, and manifold learning-based reconstruction
11. Machine learning for MRI reconstructionPART 3 Reconstruction methods for nonlinear forward models in MRI
12. Imaging in the presence of magnetic field inhomogeneities
13. Motion-corrected reconstruction
14. Chemical shift encoding-based water-fat separation
15. Model-based parametric mapping reconstruction
16. Quantitative susceptibility-mapping reconstructionAPPENDIX A Linear algebra primer
Product details
- No. of pages: 516
- Language: English
- Copyright: © Academic Press 2022
- Published: November 4, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780128227268
- eBook ISBN: 9780128227466
About the Editors
Mehmet Akcakaya
Affiliations and Expertise
Mariya Doneva
Affiliations and Expertise
Claudia Prieto
Affiliations and Expertise
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