Titlebook: Statistical Analysis of Noise in MRI; Modeling, Filtering Santiago Aja-Fernández,Gonzalo Vegas-Sánchez-Ferre Book 2016 Springer Internatio

ALE

Genetics

脱离

Noise Filtering in MRIunder very different denominations: noise filtering, noise removal, denoising, noise reduction and signal estimation. Here, we analyze and categorize the different approaches and evaluate their goodness for specific purposes: the selection of a method must be based on the precise needs of the proble

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Noise Estimation in the Complex Domainn noise. In MRI acquisitions, noise is assumed to be a Gaussian process that equally affects to all the frequencies in the .-space, independent and identically distributed for both the real and imaginary components. Thus, the stationary Gaussian assumption is also valid for each coil in the .-space,

Agnosia

Noise Estimation in Single-Coil MR Datas not depend on the position. Most of the approaches to noise estimation in MRI in the literature are precisely focused on this kind of noise. The different methods are systematized and classified attending their nature. As a case study, a special kind of estimators, those based on the calculation o

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Parametric Noise Analysis in Parallel MRI in the x-space presents some artifacts that must be corrected with the so-called pMRI methods. Among them, SENSE and GRAPPA are two of the most popular. In most pMRI methods, the reconstruction process yields to a value of the variance of noise in the final image that depends on the position. Hence

ANTIC

Blind Estimation of Non-stationary Noise in MRIe. As a consequence of the different algorithms, noise in the reconstructed signal becomes non-stationary, i.e., the variance of noise . becomes dependent on the position and, sometimes, also on the signal. Some methods have been proposed in the previous chapters to deal with non-stationary noise in

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