Main description:

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 useful MRI datasets.

Contents:

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