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Magnetic Resonance Image Reconstruction

Theory, Methods, and Applications

  • 1st Edition, Volume 7 - November 4, 2022
  • Latest edition
  • Editors: Mehmet Akcakaya, Mariya Ivanova Doneva, Claudia Prieto
  • Language: English

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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Description

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.

Key features

  • 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

Readership

MRI researchers with a background either in physics, computer science, biomedical engineering, biophysics, mathematics. Radiologists, clinicians

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

Product details

  • Edition: 1
  • Latest edition
  • Volume: 7
  • Published: November 11, 2022
  • Language: English

About the editors

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, USA

MD

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, Germany

CP

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, UK

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