Magnetic Resonance Image Reconstruction

Theory, Methods, and Applications
Erscheinungsjahr 2022
ISBN: 978-0-12-822726-8
Verlag: William Andrew Publishing

Buch, Englisch, 516 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 1000 g

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.

Autoren/Hrsg.

Akcakaya, Mehmet

Doneva, Mariya Ivanova

Prieto, Claudia

Fachgebiete

• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Bildgebende Verfahren, Nuklearmedizin, Strahlentherapie Magnetresonanztomographie, Computertomographie (MRT, CT)
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning
• Naturwissenschaften Physik Elektromagnetismus Mikroskopie, Spektroskopie
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe

Inhaltsverzeichnis

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

Über Autor(innen)

Prieto, Claudia
Dr Claudia Prieto is a Reader in the School of Biomedical Engineering & Imaging Sciences

Akcakaya, Mehmet
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.

Doneva, Mariya Ivanova
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.