Topics and features:

• Provides a complete framework for the modeling and analysis of noise in MRI, considering different modalities and acquisition techniques
• Describes noise and signal estimation for MRI from a statistical signal processing perspective
• Surveys the different methods to remove noise in MRI acquisitions, under different approaches and from a practical point of view
• Reviews different techniques for estimating noise from MRI data in single- and multiple-coil systems for fully sampled acquisitions
• Examines the issue of noise estimation when accelerated acquisitions are considered, and parallel imaging methods are used to reconstruct the signal
• Includes appendices covering probability density functions, combinations of random variables used to derive estimators, and useful MRI datasets

This practically-focused work serves as a reference manual for researchers dealing with signal processing in MRI acquisitions, and is also suitable as a textbook for postgraduate students in engineering with an interest in medical image processing.

Dr. Santiago Aja-Fernández is an Associate Professor at the School of Telecommunications of the University of Valladolid, Spain. His other publications include the Springer title Tensors in Image Processing and Computer Vision . Dr. Gonzalo Vegas-Sánchez-Ferrero is a Research Fellow at Brigham and Women's Hospital, and in the Applied Chest Imaging Laboratory of Harvard Medical School, Boston, MA, USA.