Statistical representation of mean diffusivity and fractional anisotropy brain maps of normal subjects

Siamak Ardekani, Usha Sinha

Research output: Contribution to journalArticlepeer-review


Purpose: To create diffusion tensor atlases from echo planar imaging (EPI) images acquired at 3 T in 10 normal subjects. Materials and Methods: Data from 10 right-handed healthy adult volunteers (mean age of 31 ± 3 years; eight males) were acquired using a 3.0-T scanner. Geometric distortion artifacts correction was accomplished by combining parallel acquisition to reduce the distortion as well as postprocessing by registration to a geometrically accurate T2-weighted fast-spin-echo image. This reduced distortions to within a voxel for most of the internal structures of the brain. The apparent diffusion coefficient (ADC) and fractional anisotropy (FA) atlases were created by warping images using an iterative optical-flow-based local deformation algorithm that used two channels of data: ADC and FA. Results: A three-dimensional distance measure was used to evaluate the accuracy of the registration algorithm with contours defined on two structures: the corpus callosum and cerebellum. The average three-dimensional distance value for the nine subjects (with the 10th as the reference) was 0.2 mm for the corpus callosum and 1.2 mm for the cerebellum. Conclusion: A high-resolution, diffusion MR atlas with full brain coverage was developed. Additionally, maps of the SD of the diffusion indices were also generated to provide an estimate of the variance within a normal population. Active shape and texture models were also generated for the corpus callosum as an alternate method of representing the variance in morphology and diffusion indices.

Original languageEnglish (US)
Pages (from-to)1243-1251
Number of pages9
JournalJournal of Magnetic Resonance Imaging
Issue number6
StatePublished - Dec 2006


  • 3 T
  • Active shape and texture models
  • Average atlases
  • Diffusion tensor imaging
  • Local deformation algorithm

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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