Characterization and Propagation of Uncertainty in Diffusion-Weighted MR Imaging

Timothy E.J. Behrens, M. W. Woolrich, Mi Jenkinson, H. Johansen-Berg, R. G. Nunes, S. Clare, P. M. Matthews, J. M. Brady, S. M. Smith

Research output: Contribution to journalArticlepeer-review

2046 Citations (Scopus)


A fully probabilistic framework is presented for estimating local probability density functions on parameters of interest in a model of diffusion. This technique is applied to the estimation of parameters in the diffusion tensor model, and also to a simple partial volume model of diffusion. In both cases the parameters of interest include parameters defining local fiber direction. A technique is then presented for using these density functions to estimate global connectivity (i.e., the probability of the existence of a connection through the data field, between any two distant points), allowing for the quantification of belief in tractography results. This technique is then applied to the estimation of the cortical connectivity of the human thalamus. The resulting connectivity distributions correspond well with predictions from invasive tracer methods in nonhuman primate.

Original languageEnglish
Pages (from-to)1077-1088
Number of pages12
JournalMagnetic Resonance in Medicine
Issue number5
Publication statusPublished or Issued - Nov 2003
Externally publishedYes


  • Diffusion-weighted MRI
  • Probability density functions

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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