Analyzing Rice distributed functional magnetic resonance imaging data: a Baysian approach

Lieve Lauwers, Kurt Barbé, Wendy Van Moer, Rik Pintelon

Research output: Contribution to journalArticle


Analyzing functional MRI data is often a hard task due to the fact that these periodic signals are strongly disturbed with noise. In many cases, the signals are buried under the noise and not visible, such that detection is quite impossible. However, it is well known that the amplitude measurements of such disturbed signals follow a Rice distribution which is characterized by two parameters. In this paper, an alternative Bayesian approach is proposed to tackle this two-parameter estimation problem. By incorporating prior knowledge into a mathematical framework, the drawbacks of the existing methods (i.e. the maximum likelihood approach and the method of moments) can be overcome. The performance of the proposed Bayesian estimator is analyzed theoretically and illustrated through simulations. Finally, the developed approach is successfully applied to measurement data for the analysis of functional MRI
Original languageEnglish
Number of pages12
JournalMeasurement Science and Technology
Publication statusPublished - 1 Nov 2010


  • amplitude measurements
  • Bayesian estimator
  • maximum likelihood estimator
  • Rice distribution
  • Rician distribution
  • signal detection
  • functional MRI
  • functional magnetic resonance imaging


Dive into the research topics of 'Analyzing Rice distributed functional magnetic resonance imaging data: a Baysian approach'. Together they form a unique fingerprint.

Cite this