Noise estimation in single- and multiple-coil magnetic resonance data based on statistical models.

Abstract:

:Noise estimation is a challenging task in magnetic resonance imaging (MRI), with applications in quality assessment, filtering or diffusion tensor estimation. Main noise estimators based on the Rician model are revisited and classified in this article, and new useful methods are proposed. Additionally, all the surveyed estimators are extended to the noncentral chi model, which applies to multiple-coil MRI and some important parallel imaging algorithms for accelerated acquisitions. The proposed new noise estimation procedures, based on the distribution of local moments, show better performance in terms of smaller variance and unbiased estimation over a wide range of experiments, with the additional advantage of not needing to explicitly segment the background of the image.

journal_name

Magn Reson Imaging

authors

Aja-Fernández S,Tristán-Vega A,Alberola-López C

doi

10.1016/j.mri.2009.05.025

subject

Has Abstract

pub_date

2009-12-01 00:00:00

pages

1397-409

issue

10

eissn

0730-725X

issn

1873-5894

pii

S0730-725X(09)00140-4

journal_volume

27

pub_type

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