Instructor Kwan-Jin Jung, Ph.D. (Carnegie Mellon University) Technical Assistant Nidhi Kohli...
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Transcript of Instructor Kwan-Jin Jung, Ph.D. (Carnegie Mellon University) Technical Assistant Nidhi Kohli...
DTI ModuleMNTP 2011
InstructorKwan-Jin Jung, Ph.D.
(Carnegie Mellon University)
Technical AssistantNidhi Kohli
(Carnegie Mellon University)
David Schaeffer (University of Georgia)
Lauren Libero (University of Alabama at Birmingham)
Sara Levens, Ph.D.(University of Pittsburgh)
LEARNING OBJECTIVES
Effects of Segmented sampling Motion correction Fiber orientation estimation method fMRI based ROIs vs. drawing ROIs
Anatomical separation of sensorimotor cortex
TERMINOLOGY
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
METHOD OF ACQUISITION
Segmented sampling Complementary diffusion encoding
directions 64 (A) - 10 min 64 (B) - 10 min 128 (A + B) - 20 min
Useful for special populations
MOTION CORRECTION
How to correct:1. Estimate the motion2. Rotate image and vector table accordingly
Intended Collected Head correction WRONG
Head & vector table correction
CORRECT
MOTION CORRECTION
No correction No vector rotation
Interpolation Estimates how much you rotate vector
table Based on distributed b0 images – “real
motion”
Rota
tion
(degre
es)
Rota
tion
(degre
es)
Time
BEFORE
AFTER
6
3
0
-3
-6
Time
6
3
0
-3
-6
MOTION CORRECTION
Simulation method Collect two diffusion scans1. 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
Low b-value (b=800
s/mm²) DWI(scan 1)
Assume no motion
Co-registervolumes
(estimating motion)
High b-value (b=2000
s/mm²) DWI(scan 2)
Find D (diffusion tensor)
S=S0e-
bD
Find S (simulated
high b-value)
S=S0e-
bD
Rotate vector table
FIBER ORIENTATION ESTIMATION METHODFiber/voxel Data Acquisition Analysis
Single fiber 6 – 12 directions Tensor
Multiple fibers > 25 directions (HARDI)
CSD (Q-ball, multi-tensor)
FIBER ORIENTATION ESTIMATION METHOD 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)
CSD VS. TENSOR
CSD Tensor0
20000
40000
60000
80000
100000
120000
Average Number of Tracts in Genu
N F
ibers
GenuTensor
GenuCSD
DRAWING ROIS 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
SEGMENTING SENSORIMOTOR
Finger closing fMRI results as ROI Separation of sensory and motor areas
Clustering – fiber end-point distribution
Central Sulcus
SUMMARY
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
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
ACKNOWLEDGMENTS
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