This unique text presents a comprehensive review of methods for modeling signal and noise in magnetic resonance imaging (MRI), providing a systematic study, classifying and comparing the numerous and varied estimation and filtering techniques. 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 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 usefulMRI datasets.

Provides comprehensive coverage of the field within a single, unified framework

The Problem of Noise in MRI.- Part I: Noise Models and the Noise Analysis Problem.- Acquisition and Reconstruction of Magnetic Resonance Imaging.- Statistical Noise Models for MRI.- Noise Analysis in MRI: Overview.- Noise Filtering in MRI.- Part II: Noise Analysis in Non-Accelerated Acquisitions.- Noise Estimation in the Complex Domain.- Noise Estimation in Single-Coil MR Data.- Noise Estimation in Multiple-Coil MR Data.- Parametric Noise Analysis from Correlated Multiple-Coil MR Data.- Part III: Noise Estimators in pMRI.- Parametric Noise Analysis in Parallel MRI.- Blind Estimation of Non-Stationary Noise in MRI.- Appendix A: Probability Distributions and Combination of Random Variables.- Appendix B: Variance Stabilizing Transformation.- Appendix C: Data Sets Used in the Experiments.