DocumentCode
650054
Title
Rethinking MRI random signals modeling
Author
Vianney Kinani, Jean Marie ; Rosales-Silva, Alberto J. ; Gallegos-Funes, Francisco J. ; Arellano, Alfonso
Author_Institution
Escuela Super. de Ing. Mec. y Electr., Inst. Politec. Nac., Mexico City, Mexico
fYear
2013
fDate
Sept. 30 2013-Oct. 4 2013
Firstpage
116
Lastpage
121
Abstract
Based on both the Physics of MRI and the central limit theorem, it is common practice to assume that the noise in MR images is Gauss distributed, but from an MR signal post-acquisition standpoint, this modeling approach can be proved to be erroneous, especially when the SNR is low. In this article, we present a thorough analysis that shows why the Gaussian model was adopted, and through the MR complex raw data post-acquisition mathematical treatment, the Rician model will be developed and proved to be the right MR random signals model.
Keywords
Gaussian noise; biomedical MRI; image denoising; Gauss distributed noise; Gaussian model; MR complex raw data post-acquisition mathematical treatment; MR image noise; MR signal post-acquisition standpoint; MRI random signals modeling; Rician model; central limit theorem; Gaussian; MRI; PDF; Rice distributions; SNR; noise;
fLanguage
English
Publisher
ieee
Conference_Titel
Electrical Engineering, Computing Science and Automatic Control (CCE), 2013 10th International Conference on
Conference_Location
Mexico City
Print_ISBN
978-1-4799-1460-9
Type
conf
DOI
10.1109/ICEEE.2013.6676085
Filename
6676085
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