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Statistical Analysis of Noise in MRI (eBook, PDF) - Aja-Fernández, Santiago; Vegas-Sánchez-Ferrero, Gonzalo
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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…mehr

Produktbeschreibung
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.
Rezensionen
"The book is presented in a simple and lucid manner, starting with the basics of MRI noise and its analysis with simple models, progressing to an analysis using complex models and the noise issues in multi-coil and parallel acquisition schemes. Overall the book is self-contained to help the beginners ... ." (Pramod Kumar Pisharady, IAPR Newsletter , Vol. 40 (2), 2018)