This book is about modern approaches to magnetic resonance imaging (MRI) reconstruction. In the last decade, MRI has benefitted immensely from advances in applied mathematics and signal processing. Leveraging these techniques, MRI scans are now being performed two to four times faster than before. In this book, we learn how these techniques have been used in the recent past to accelerate MRI scans.

During my PhD, I worked on a few different areas of MRI reconstruction – static MRI, dynamic MRI, parallel MRI (static and dynamic) and quantitative MRI. After I relocated to India, Manish Chaudhury commissioning editor at Cambridge University Press, inspired me to write a book and I was eager to write about signal processing techniques in MRI. It took me about one and half years to complete this volume.

When I started working on MRI reconstruction, I felt that there is a gap between the practitioners and the theoreticians. On one side, there were researchers in signal processing and applied maths who were interested in theoretical proofs and algorithms. On the other, there were the MRI physicists and engineers who had lots of interesting problems that were waiting to be solved. Since then, many researchers have worked very hard to reduce this gap. The concerted effort of so many researchers is finally bearing fruit; in the past few ISMRMs, MRI scanner manufacturers showed interest in adopting these advanced signal processing techniques for image reconstruction.

In this book, I have made every effort to incorporate interesting studies on MRI reconstruction, but I may have missed out a few unintentionally. Thus, this book does not claim to be an encyclopaedic review on the subject of signal processing techniques in MRI reconstruction.

The targeted audience of the book are signal processing engineers who want to learn about MRI problems and MRI physicists who want to know how signal processing is benefitting MRI. The book can also be perused by doctors who have a background in mathematics. I do not presume a reader who has an advanced background in mathematics. But the reader is expected to have some undergraduate training in linear algebra, probability and convex optimization. Otherwise, the book may not be easy to follow.