Magnetic Resonance Image Reconstruction: Theory, Methods and Applications presents the fundamental concepts of MR image reconstruction, including its formulation as an inverse problem, as well as the most common models and optimization methods for reconstructing MR images. The book discusses approaches for specific applications such as non-Cartesian imaging, under sampled reconstruction, motion correction, dynamic imaging and quantitative MRI. This unique resource is suitable for physicists, engineers, technologists and clinicians with an interest in medical image reconstruction and MRI.
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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 methods
PART 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 reconstruction
PART 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 reconstruction
APPENDIX A Linear algebra primer
