• 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