Statistical Analysis Of Noise In Mri: Modeling, Filtering And Estimation
by Santiago Aja-Fernández /
2016 / English / PDF
14.4 MB Download
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.
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.
