Parallel MRI Noise Correction: An Extension of the LMMSE to Non Central χ Distributions

  • Véronique Brion
  • Cyril Poupon
  • Olivier Riff
  • Santiago Aja-Fernández
  • Antonio Tristán-Vega
  • Jean-François Mangin
  • Denis Le Bihan
  • Fabrice Poupon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6892)

Abstract

Parallel MRI leads to magnitude data corrupted by noise described in most cases as following a Rician or a non central χ distribution. And yet, very few correction methods perform a non central χ noise removal. However, this correction step, adapted to the correct noise model, is of very much importance, especially when working with Diffusion Weighted MR data yielding a low SNR. We propose an extended Linear Minimum Mean Square Error estimator (LMMSE), which is adapted to deal with non central χ distributions. We demonstrate on simulated and real data that the extended LMMSE outperforms the original LMMSE on images corrupted by a non central χ noise.

Keywords

Noise Standard Deviation Rician Noise Parallel Magnetic Resonance Image Linear Minimum Mean Square Error Estimator Element Phase Array Antenna 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Véronique Brion
    • 1
    • 2
  • Cyril Poupon
    • 1
    • 2
  • Olivier Riff
    • 1
    • 2
  • Santiago Aja-Fernández
    • 3
  • Antonio Tristán-Vega
    • 3
  • Jean-François Mangin
    • 1
    • 2
  • Denis Le Bihan
    • 1
    • 2
  • Fabrice Poupon
    • 1
    • 2
  1. 1.CEA I2BM NeuroSpin, Gif-sur-YvetteFrance
  2. 2.IFR 49ParisFrance
  3. 3.LPIUniversidad de ValladolidSpain

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