ROBOTICS & AUTOMATION
Empowering Progress
Up to 25% off Essentials Robotics and Automation titles
Magnetic Resonance Image Reconstruction
Theory, Methods, and Applications
- 1st Edition, Volume 7 - November 4, 2022
- 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
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteMagnetic 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.
- 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
MRI researchers with a background either in physics, computer science, biomedical engineering, biophysics, mathematics. Radiologists, clinicians
- 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
- No. of pages: 516
- Language: English
- Edition: 1
- Volume: 7
- Published: November 4, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780128227268
- eBook ISBN: 9780128227466
MA
Mehmet Akcakaya
Mehmet Akçakaya was born in Istanbul, Turkey. He went to Robert College for high school, moved to Montreal for undergraduate studies at McGill University, where he graduated with great distinction and Charles Michael Morssen Gold Medal. He got his PhD degree in May 2010 under the supervision of Professor Vahid Tarokh in the School of Engineering and Applied Sciences (SEAS), Harvard University.He was a post-doctoral fellow at BIDMC CMR Center between 2010-12, and an Instructor in Medicine at Harvard Medical School between 2012-15.
Affiliations and expertise
McGill University, USAMD
Mariya Ivanova Doneva
Mariya Doneva is a senior scientist at Philips Research, Hamburg, Germany. She received her BSc and MSc degrees in Physics from the University of Oldenburg in 2006 and 2007, respectively and her PhD degree in Physics from the University of Luebeck in 2010. She was a Research Associate at Electrical Engineering and Computer Sciences department at UC Berkeley between 2015 and 2016. She is a recipient of the Junior Fellow award of the International Society for Magnetic Resonance in Medicine. Her research interests include methods for efficient data acquisition, image reconstruction and quantitative parameter mapping in the context of magnetic resonance imaging.
Affiliations and expertise
Senior Scientist, Philips Research, GermanyCP
Claudia Prieto
Dr Claudia Prieto is a Reader in the School of Biomedical Engineering & Imaging Sciences
Affiliations and expertise
Reader, School of Biomedical Engineering and Imaging Sciences, UKRead Magnetic Resonance Image Reconstruction on ScienceDirect