Statistical Analysis of Noise in MRI : Modeling, Filtering and Estimation / by Santiago Aja-Fernández, Gonzalo Vegas-Sánchez-Ferrero.

Format:

Book

Author/Creator:

Aja-Fernández, Santiago, author.

Vegas-Sánchez-Ferrero, Gonzalo, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Mathematical statistics.

Statistics.

Optical data processing.

Computer simulation.

Biomedical engineering.

Probability and Statistics in Computer Science.

Statistics for Life Sciences, Medicine, Health Sciences.

Image Processing and Computer Vision.

Simulation and Modeling.

Biomedical Engineering and Bioengineering.

Local Subjects:

Probability and Statistics in Computer Science.

Statistics for Life Sciences, Medicine, Health Sciences.

Image Processing and Computer Vision.

Simulation and Modeling.

Biomedical Engineering and Bioengineering.

Physical Description:

1 online resource (XXI, 327 pages) : 172 illustrations, 99 illustrations in color

Edition:

First edition 2016.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2016.

System Details:

text file PDF

Summary:

This unique text/reference 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 drawn from more than ten years of research in this area. 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.

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.

Other Format:

Printed edition:

ISBN:

978-3-319-39934-8

9783319399348

Access Restriction:

Restricted for use by site license.