Magnetic Resonance Image Reconstruction

Theory, Methods, and Applications

1st Edition, Volume 7 - November 4, 2022
Imprint: Academic Press
Editors: Mehmet Akcakaya, Mariya Ivanova Doneva, Claudia Prieto
Language: English
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 2 2 7 2 6 - 8
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 2 2 7 4 6 - 6

Magnetic Resonance Image Reconstruction: Theory, Methods and Applications presents the fundamental concepts of MR image reconstruction, including its formulation as an invers… Read more

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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.

Cover image
Title page
Table of Contents
Copyright
Contributors
Editor Biographies
Introduction
Chapter 1: Brief Introduction to MRI Physics
Abstract
1.1. A brief history of MRI
1.2. Nuclear magnetism
1.3. NMR/MRI signal
1.4. Image formation
1.5. Components of an MRI scanner
1.6. Summary
References
Suggested readings
Chapter 2: MRI Reconstruction as an Inverse Problem
Abstract
2.1. Inverse problems
2.2. Discretization of the MR signal
2.3. MR reconstruction as a linear inverse problem
2.4. Solution of the MR reconstruction problem
2.5. Regularizing the MR reconstruction problem
2.6. Nonlinear inverse problems in MR
2.7. Summary
References
Suggested readings
Chapter 3: Optimization Algorithms for MR Reconstruction
Abstract
3.1. Introduction
3.2. Least squares reconstruction
3.3. Model-based reconstruction
3.4. Summary
References
Chapter 4: Non-Cartesian MRI Reconstruction
Abstract
4.1. Introduction
4.2. NFFT
4.3. Gridding
4.4. Iterative reconstruction
4.5. Examples
4.6. Spatial resolution and noise
4.7. Extensions
4.8. Summary
References
Chapter 5: “Early” Constrained Reconstruction Methods
Abstract
5.1. Introduction
5.2. Support-constrained reconstruction
5.3. Phase-constrained reconstruction
5.4. Linear predictive reconstruction
5.5. Rank-constrained reconstruction
5.6. Sparsity-constrained reconstruction
5.7. Reconstruction using side information
5.8. Discussion
5.9. Summary
References
Chapter 6: Parallel Imaging
Abstract
6.1. Introduction
6.2. Fundamental techniques
6.3. Advanced techniques
6.4. 3D volumetric parallel imaging
6.5. Dynamic parallel imaging
6.6. Artifacts in parallel imaging
6.7. Summary
References
Suggested readings
Chapter 7: Simultaneous Multislice Reconstruction
Abstract
7.1. Introduction
7.2. Basics of SMS encoding
7.3. Reconstruction of SMS using parallel imaging concepts
7.4. Calibration and reference scans
7.5. Reconstruction metrics
7.6. Extensions of SMS
7.7. Applications of SMS
7.8. Summary
7.9. Exercise
Appendix 7.A. Extended FOV methods for SMS
References
Chapter 8: Sparse Reconstruction
Abstract
8.1. Introduction
8.2. Compressed sensing theory: a brief overview
8.3. Compressed sensing MRI
8.4. Combination of compressed sensing MRI with parallel imaging
8.5. Clinical applications of compressed sensing MRI
8.6. Challenges of compressed sensing MRI
8.7. Summary
8.8. Tutorial
Acknowledgements
Appendix 8.A. Conditions for a unique solution in compressed sensing
References
Chapter 9: Low-Rank Matrix and Tensor–Based Reconstruction
Abstract
9.1. Introduction
9.2. Problem formulation
9.3. Matrix-based approaches
9.4. Tensor-based approaches
9.5. Summary
References
Chapter 10: Dictionary, Structured Low-Rank, and Manifold Learning-Based Reconstruction
Abstract
10.1. Introduction
10.2. Background
10.3. Dictionary learning and blind compressed sensing
10.4. Structured low-rank methods
10.5. Smooth manifold models
10.6. Software
10.7. Summary
References
Chapter 11: Machine Learning for MRI Reconstruction
Abstract
11.1. Introduction
11.2. Organization of this chapter
11.3. Machine learning definitions
11.4. Task definition for MR reconstruction
11.5. Core concepts: layers
11.6. Network architectures for MRI reconstruction
11.7. How to build an ML model for MR reconstruction
11.8. Summary
11.9. Further resources and tutorials
11.10. Exercises
Appendix 11.A. ML-specific notation
Appendix 11.B. Complex calculus
Appendix 11.C. Trainable parameters of separable convolutions
References
Chapter 12: Imaging in the Presence of Magnetic Field Inhomogeneities
Abstract
12.1. Introduction
12.2. Disruptions to the homogeneity of the magnetic field
12.3. Field inhomogeneity effects on imaging
12.4. Image distortions and correction approaches
12.5. Phase and signal dephasing correction approaches
12.6. K-space trajectory distortions
12.7. Measuring the field map
12.8. Summary
References
Chapter 13: Motion-Corrected Reconstruction
Abstract
13.1. Introduction
13.2. Theory
13.3. Methods
13.4. Clinical application examples
13.5. Current challenges and future directions
13.6. Summary
13.7. Practical tutorial
References
Chapter 14: Chemical Shift Encoding-Based Water-Fat Separation
Abstract
14.1. Introduction
14.2. Theory on chemical species separation
14.3. Solving the water–fat separation problem
14.4. Water–fat separation in non-Cartesian imaging
14.5. Confounding factors in quantitative water–fat imaging
14.6. Current challenges and future directions
14.7. Summary
14.8. Further reading
References
Chapter 15: Model-Based Parametric Mapping Reconstruction
Abstract
15.1. Introduction
15.2. MR mapping sequences
15.3. Image-based mapping
15.4. Reconstruction-based mapping
15.5. Clinical applications
15.6. Current challenges and future directions
15.7. Summary
15.8. Tutorial
Acknowledgement
References
Chapter 16: Quantitative Susceptibility-Mapping Reconstruction
Abstract
16.1. Introduction
16.2. GRE data acquisition
16.3. Phase pre-processing
16.4. Dipole inversion
16.5. Recent advances: single-step QSM and deep-learning-based QSM
16.6. Summary and outlook
Appendix 16.A. Tutorials
References
Appendix A: Linear Algebra Primer
Index

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Magnetic Resonance Image Reconstruction

Theory, Methods, and Applications

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Mehmet Akcakaya

Mariya Ivanova Doneva

Claudia Prieto

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