Magnetic Resonance Image Reconstruction

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. 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. Claudia Prieto, PhD, is a Professor at the Faculty of Engineering at the Pontificia Universidad Católica de Chile. She is an internationally recognized expert in cardiac magnetic resonance imaging (MRI), with more than 15 years of experience in MRI research. Her work focuses on developing and evaluating novel MRI techniques for improved assessment of cardiovascular disease, including acquisition, reconstruction, motion estimation and correction, quantitative MRI, and AI–based solutions for different stages of the imaging pipeline.

• PART 1 Basics of MRI Reconstruction 1. Brief introduction to MRI physics 2. MRI reconstruction as an inverse problem3. Optimization algorithms for MR reconstruction4. Non-Cartesian MRI reconstruction5. “Early” constrained reconstruction methodsPART 2 Reconstruction of undersampled MRI data6. Parallel imaging7. Simultaneous multislice reconstruction8. Sparse reconstruction9. Low-rank matrix and tensor–based reconstruction10. Dictionary, structured low-rank, and manifold learning-based reconstruction11. Machine learning for MRI reconstructionPART 3 Reconstruction methods for nonlinear forward models in MRI12. Imaging in the presence of magnetic field inhomogeneities13. Motion-corrected reconstruction14. Chemical shift encoding-based water-fat separation15. Model-based parametric mapping reconstruction16. Quantitative susceptibility-mapping reconstructionAPPENDIX A Linear algebra primer