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

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

3.  Diffusion encoding gradient direction  Vector table (x, y, z components)  Angular resolution  Diffusion-weighting (b-values)  Duration & amplitude  s/mm ²  b0 = 0 s/mm ²  No diffusion gradient

4.  Segmented sampling  Complementary diffusion encoding directions  64 (A) - 10 min  64 (B) - 10 min  128 (A + B) - 20 min  Useful for special populations

5.  How to correct: 1. Estimate the motion 2. Rotate image and vector table accordingly IntendedCollectedHead 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” Rotation (degrees) Time BEFOREAFTER 6 3 0 -3 -6 Time 6 3 0 -3 -6

7.  Simulation method  Collect two diffusion scans 1. 6 direction scan (low b-value)  Why? – Fast (little time for motion)  Edges of brain are clearly defined 2. 6 or more direction scan (higher b-value)  Assume no motion on scan 1, then simulate what higher b-value volume should look like

8. Low b-value (b=800 s/mm ² ) DWI (scan 1) Assume no motion Co-register volumes (estimating motion) High b-value (b=2000 s/mm ² ) DWI (scan 2) Find D (diffusion tensor) S=S 0 e -bD Find S (simulated high b-value) S=S 0 e -bD Rotate vector table

9. Fiber/voxelData AcquisitionAnalysis Single fiber6 – 12 directionsTensor Multiple fibers> 25 directions (HARDI)CSD (Q-ball, multi-tensor)

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 (Tournier et al., 2007)

11. Genu Tensor Genu CSD

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

13.  Finger closing fMRI results as ROI  Separation of sensory and motor areas  Clustering – fiber end-point distribution 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

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

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

19. No correctionInterpolationSimulation Motion