Modeling water diffusion anisotropy within fixed newborn primate brain using Bayesian probability theory

Christopher D. Kroenke, G. Larry Bretthorst, Terrie E. Inder, Jeffrey J. Neil

Research output: Contribution to journalArticlepeer-review

45 Scopus citations


An active area of research involves optimally modeling brain diffusion MRI data for various applications. In this study Bayesian analysis procedures were used to evaluate three models applied to phase-sensitive diffusion MRI data obtained from formalin-fixed perinatal primate brain tissue: conventional diffusion tensor imaging (DTI), a cumulant expansion, and a family of modified DTI expressions. In the latter two cases the optimum expression was selected from the model family for each voxel in the image. The ability of each model to represent the data was evaluated by comparing the magnitude of the residuals to the thermal noise. Consistent with previous findings from other laboratories, the DTI model poorly represented the experimental data. In contrast, the cumulant expansion and modified DTI expressions were both capable of modeling the data to within the noise using six to eight adjustable parameters per voxel. In these cases the model selection results provided a valuable form of image contrast. The successful modeling procedures differ from the conventional DTI model in that they allow the MRI signal to decay to a positive offset. Intuitively, the positive offset can be thought of as spins that are sufficiently restricted to appear immobile over the sampled range of b-values.

Original languageEnglish (US)
Pages (from-to)187-197
Number of pages11
JournalMagnetic Resonance in Medicine
Issue number1
StatePublished - Jan 2006
Externally publishedYes


  • Bayesian probability theory
  • Brain
  • Diffusion ansiotropy
  • MRI
  • Newborn

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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