Statistical analysis of noise in MRI : modeling, filtering and estimation

著者

    • Aja-Fernández, Santiago
    • Vegas-Sánchez-Ferrero, Gonzalo

書誌事項

Statistical analysis of noise in MRI : modeling, filtering and estimation

Santiago Aja-Fernández, Gonzalo Vegas-Sánchez-Ferrero

Springer, c2016

  • : hardcover

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注記

Includes bibliographical references and index

内容説明・目次

内容説明

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.

目次

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

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