DTI Module MNTP 2011

1. David Schaeffer (University of Georgia) Lauren Libero (University of Alabama at Birmingham) Sara Levens, Ph.D. (University of Pittsburgh) DTI ModuleMNTP 2011 Instructor Kwan-Jin Jung, Ph.D. (Carnegie Mellon University) Technical Assistant NidhiKohli (Carnegie Mellon University)

2. Effects of • Segmented sampling • Motion correction • Fiber orientation estimation method • fMRI based ROIs vs. drawing ROIs • Anatomical separation of sensorimotor cortex Learning Objectives

3. Diffusion encoding gradient direction • Vector table (x, y, z components) • Angular resolution • Diffusion-weighting (b-values) • Duration & amplitude • s/mm² • b0 = 0 s/mm² • No diffusion gradient Terminology

4. Segmented sampling • Complementary diffusion encoding directions • 64 (A) - 10 min • 64 (B) - 10 min • 128 (A + B) - 20 min • Useful for special populations Method of Acquisition

5. How to correct: 1. Estimate the motion 2.Rotate image and vector table accordingly Motion Correction Intended Collected Head correction WRONG Head & vector table correction CORRECT

6. No correction • No vector rotation • Interpolation • Estimates how much you rotate vector table • Based on distributed b0 images – “real motion” Motion Correction 6 6 Rotation (degrees) 3 3 Rotation (degrees) 0 0 -3 -3 -6 -6 Time Time BEFORE AFTER

7. Simulation method • Collect two diffusion scans • 6 direction scan (low b-value) • Why? – Fast (little time for motion) • Edges of brain are clearly defined • 6 or more direction scan (higher b-value) • Assume no motion on scan 1, then simulate what higher b-value volume should look like Motion Correction

8. Find D (diffusion tensor) S=S0e-bD Find S (simulated high b-value) S=S0e-bD Low b-value (b=800 s/mm²) DWI (scan 1) Assume no motion Co-register volumes (estimating motion) High b-value (b=2000 s/mm²) DWI (scan 2) Rotate vector table

9. Fiber orientation estimation method

10. Tensor • Performs well for straight tracts (like motor) • Performs poorly for crossing and branching fibers (like Genu) • Constrained Spherical Deconvolution (CSD) • Better for detecting branching and crossing fibers Fiber orientation estimation method (Tournier et al., 2007)

11. Csd Vs. Tensor Genu CSD Genu Tensor

12. Manually draw ROIs • Using fMRI • Collect fMRI data – find center of activation (x, y, z) • Matrix transformation • Convert from fMRI coordinates into DWI native space DrawingROIs

13. Finger closing fMRI results as ROI • Separation of sensory and motor areas • Clustering – fiber end-point distribution Segmenting Sensorimotor Central Sulcus

14. Sampling schemes can be advantageously altered for use with special populations Simulation is a promising method for more accurate motion correction CSD Fiber tracking is most appropriate for resolving fiber crossings summary

15. fMRI-based ROIs can be used to track fibers from areas of activation DTI can be used as a tool to segment brain areas that are not separable based on diffuse fMRI activation maps Summary

16. Dr. Kwan-Jin Jung • NidhiKohli • MNTP Leaders: Dr. Eddy & Dr. Kim • MTNP Trainees & Participants • DTI Trainees 2009 & 2010 • Funding: • NIH grants: R90DA023420 and T90DA022761 Acknowledgments

17. Distributed b0

18. Scanning parameters

19. motion correction No correction Interpolation Simulation Motion