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
Link To Document :
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