Magnetic Resonance Image Reconstruction: Theory, Methods, and Applications: Advances in Magnetic Resonance Technology and Applications, cartea 7
Editat de Mehmet Akcakaya, Mariya Ivanova Doneva, Claudia Prietoen Limba Engleză Paperback – 11 noi 2022
- 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
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Specificații
ISBN-13: 9780128227268
ISBN-10: 0128227265
Pagini: 516
Ilustrații: 75 illustrations (45 in full color)
Dimensiuni: 191 x 235 x 33 mm
Greutate: 0.88 kg
Editura: ELSEVIER SCIENCE
Seria Advances in Magnetic Resonance Technology and Applications
ISBN-10: 0128227265
Pagini: 516
Ilustrații: 75 illustrations (45 in full color)
Dimensiuni: 191 x 235 x 33 mm
Greutate: 0.88 kg
Editura: ELSEVIER SCIENCE
Seria Advances in Magnetic Resonance Technology and Applications
Cuprins
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
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