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Diffusion Tensor Imaging

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

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  1. Diffusion Tensor Imaging Tim Hughes & Emilie Muelly

  2. 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

  3. 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)

  4. 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

  5. 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)

  6. 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

  7. 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

  8. 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)

  9. 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

  10. Raw Data (UF) Acquired b0 Synthetic b0 Tensor b2400 CSD b2400

  11. 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.

  12. Effect of DTI parameters on mean FA value (positive correlation) * Significant interaction between b0 method and tract on mean FA value

  13. Effect of Synthetic b0 on FA Value Differences by Tract Mean FA value

  14. 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

  15. Acknowledgments • MNTP program • Seong-Gi Kim • Bill Eddy • Tomika Cohen • DTI module Mentor – Kwan-Jin Jung TA – Xiaohan Huang

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