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Diffusion Tensor Imaging. Tim Hughes & Emilie Muelly. DTI Module. Learning objectives Acquisition Fiber orientation distribution function (ODF) Tractography Projects Combining fMRI + DTI to explore face recognition & working memory Comparing and contrasting DTI parameters .

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diffusion tensor imaging

Diffusion Tensor Imaging

Tim Hughes & Emilie Muelly

dti module
DTI Module
  • Learning objectives
    • Acquisition
    • Fiber orientation distribution function (ODF)
    • Tractography
  • Projects
    • Combining fMRI + DTI to explore face recognition & working memory
    • Comparing and contrasting DTI parameters
diffusion tensor imaging1
Diffusion Tensor Imaging
  • DTI acquisition:
    • Non-diffusion weighted images
    • Diffusion weighted images (DWI)
  • Magnitude of diffusion weighting (e.g. b=1200 or 2400)

b-value : angular resolution

signal:noise

  • Output measures
    • Apparent Diffusion Co-efficient, Mean Diffusivity
    • Fractional Anisotropy (FA)
acquired b0 image
Acquired b0 image
  • Acquired b0 (b=0 s/mm2): a reference for DTI analysis
  • Problematic with partial volumes
    • Neuronal tissue
    • Free water (cerebrospinl fluid, extracellular fluid, and edema)
  • Effect on ADC, FA value, and fiber tracking
  • Partially fixed by FLAIR,
    • Incomplete saturation (mainly corrects for CSF)
    • Increased scan time
synthetic b0
Synthetic b0

= max(DW images)

  • Developed as a result of last year’s MNTP (Jung et al)
  • Uses max signal intensity (from any direction) at each voxel to create synthetic b0 image
  • Designed to minimize free water effect
  • No impact on scan time

R

  • (Image contrast enhanced using gamma corection: gamma=0.5)
fiber odf analysis methods
Fiber ODF Analysis Methods
  • Tensor model
    • Single orientation at voxel (single ODF)
    • 6+ directions with 1 b0
    • No information regarding fiber crossing
  • Constrained Spherical Deconvolution (CSD)
    • HARDI (high angular resolution diffusion imaging)
    • 23+ DW directions with multiple b0
    • Informative crossing

Tournier et al., 2007

methods acquisition pre prossessing
Methods: Acquisition & Pre-prossessing
  • 4 subjects
  • Acquire diffusion weighted images
    • Siemens 3T MRI; TR = 6900ms, TE = 115ms
    • 50 directions, 5 b0 values (across time)
    • b-values = 1200 s/mm2 or 2400 s/mm2
    • 2 acquisitions per subject, per b-value
  • Pre-process the data:
    • Motion correction (rotation of vector table)
    • Create Synthetic b0
methods odf and tractography
Methods: ODF and Tractography

Cingulum

  • ExploreDTI v4.8.0 (A. Leemans)
  • ODF analysis (Tensor or CSD)
  • Identified tracts using regions of interest
  • Obtained tract-based statistics (mean FA value, standard deviation, number of “fibers”)

Fornix

Uncinate

Fasciculus (UF)

Inferior fronto-occipital fasciculus (IFOF)

methods fiber analysis
Methods: Fiber analysis
  • Parameters
    • Diffusion weighting:
    • b0 images:
    • ODF method:
  • SAS v9.2
    • GLM, compare effects of each parameter on outcomes
    • Evaluated effects of all first order interactions on outcomes
raw data uf
Raw Data (UF)

Acquired b0

Synthetic b0

Tensor

b2400

CSD

b2400

effect of dti parameters on number of fibers
Effect of DTI parameters on number of fibers

* Tract-based analysis indicates that synthetic b0 significantly increases the number of fibers in the fornix only.

effect of dti parameters on mean fa value
Effect of DTI parameters on mean FA value

(positive correlation)

* Significant interaction between b0 method and tract on mean FA value

conclusions
Conclusions
  • Changing DTI parameters can significantly alter the number of fibers and FA values
  • Diffusion weighting
    • No significant differences in b1200 and b2400
  • b0 images
    • Synthetic b0 FA compared to acquired b0
    • Effects of both FA and # fibers are most dramatic in the fornix
  • ODF methods
    • CSD method # fibers, mean FA values compared to tensor based method
acknowledgments
Acknowledgments
  • MNTP program
    • Seong-Gi Kim
    • Bill Eddy
    • Tomika Cohen
  • DTI module

Mentor – Kwan-Jin Jung

TA – Xiaohan Huang