Normal white matter development from infancy to adulthood: comparing diffusion tensor and high b value diffusion weighted MR images

J Magn Reson Imaging. 2005 May;21(5):503-11. doi: 10.1002/jmri.20281.

Abstract

Purpose: To evaluate the sensitivity of high b value diffusion weight magnetic resonance imaging (DWI) in detecting normal white matter maturation, compare it to conventional diffusion tensor imaging (DTI), and to obtain normative quantitative data using this method.

Materials and methods: High b value DWI (b(max) = 6000 sec/mm(2)) using q-space analysis and conventional DTI (b = 1000 sec/mm(2)) were performed on 36 healthy subjects aged 4 months to 23 years. Fractional-anisotropy (FA), apparent-displacement, and apparent-probability values were measured in all slices and in six regions of interest (ROIs) of large fiber tracks. Values were correlated with each other and with age using regression analysis.

Results: FA, displacement, and probability indices from all slices were highly correlated with each other (r > 0.87, P < 0.0001) and with age (r > 0.82, P < 0.0001). All age-related changes in the six pre-determined ROIs were best fitted by mono-exponential functions. Changes in the splenium extended to a later age when compared with the genu of the corpus-callosum, while the centrum semi-ovale demonstrated the latest changes with age.

Conclusions: High b-value DWI and DTI showed changes in white matter from infancy through adulthood. However, high b-value detects a signal that is likely to originate mainly from the intra-axonal water population, and thus may represent different aspects of development and different sensitivity to pathology.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Brain / anatomy & histology*
  • Brain / growth & development
  • Chi-Square Distribution
  • Child
  • Child, Preschool
  • Cross-Sectional Studies
  • Diffusion Magnetic Resonance Imaging / methods*
  • Female
  • Humans
  • Infant
  • Male
  • Prospective Studies
  • Regression Analysis
  • Sensitivity and Specificity