Rethinking MRI random signals modeling

Jean Marie Vianney Kinani, Alberto J. Rosales-Silva, Francisco J. Gallegos-Funes, Alfonso Arellano

Resultado de la investigación: Contribución a una conferenciaArtículo

Resumen

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. © 2013 IEEE.
Idioma originalInglés estadounidense
Páginas116-121
Número de páginas103
DOI
EstadoPublicada - 1 ene 2013
Evento2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2013 -
Duración: 1 ene 2013 → …

Conferencia

Conferencia2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2013
Período1/01/13 → …

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    Kinani, J. M. V., Rosales-Silva, A. J., Gallegos-Funes, F. J., & Arellano, A. (2013). Rethinking MRI random signals modeling. 116-121. Papel presentado en 2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2013, . https://doi.org/10.1109/ICEEE.2013.6676085