B-value dependence of DTI quantitation and sensitivity in detecting neural tissue changes

Neuroimage. 2010 Feb 1;49(3):2366-74. doi: 10.1016/j.neuroimage.2009.10.022. Epub 2009 Oct 24.

Abstract

Recently, remarkable success has been demonstrated in using MR diffusion tensor imaging (DTI) to characterize white matter. Water diffusion in complex biological tissue microstructure is not a free or Gaussian process but is hindered and restricted, thus contradicting the basic assumption in conventional DTI that diffusion weighted signal decays with b-value in a monoexponential manner. Nevertheless, DTI by far is still the fastest and most robust protocol in routine research and clinical settings. To assess the b-value dependence of DTI indices and evaluate their sensitivities in detecting neural tissues changes, in vivo DTI data acquired from rat brains at postnatal day 13, 21 and 120 with different b-values (0.5-2.5 ms/microm(2)) and 30 gradient directions were analyzed. Results showed that the mean and directional diffusivities consistently decreased with b-value in both white and gray matters. The sensitivity of axial diffusivity (lambda(//)) in monitoring brain maturation generally decreased with b-value whereas that of radial diffusivity (lambda( perpendicular)) increased. FA generally varied less with b-value but in a manner dependent of the age and tissue type. Analysis also revealed that the FA sensitivity in detecting specific tissue changes was affected by b-value. These experimental findings confirmed the crucial effect of b-value on quantitative DTI in monitoring neural tissue alterations. They suggested that the choice of b-value in conventional DTI acquisition can be optimized for detecting neural tissue changes but shall depend on the specific tissue type and its changes or pathologies targeted, and caution must be taken in interpreting DTI indices.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Brain / growth & development*
  • Brain / physiology*
  • Diffusion Tensor Imaging*
  • Image Processing, Computer-Assisted
  • Rats
  • Sensitivity and Specificity