Transcript DTI Module
David Schaeffer
Lauren Libero
(University of Georgia)
(University of Alabama at Birmingham)
Sara Levens, Ph.D.
(University of Pittsburgh)
Instructor
Kwan-Jin Jung, Ph.D.
Technical Assistant
Nidhi Kohli
(Carnegie Mellon University)
(Carnegie Mellon University)
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 motion
2. Rotate image and vector table accordingly
Intended
Collected
Head correction
WRONG
Head & vector table
correction
CORRECT
MOTION CORRECTION
No correction
No
vector rotation
Interpolation
how much you rotate vector table
Based on distributed b0 images – “real motion”
6
Rotation (degrees)
Rotation (degrees)
Estimates
3
0
-3
-6
Time
6
3
0
-3
-6
Time
BEFORE
AFTER
MOTION CORRECTION
Simulation method
Collect
1.
two diffusion scans
6 direction scan (low b-value)
2.
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
Low b-value
(b=800 s/mm²)
DWI
(scan 1)
Find D (diffusion
tensor)
S=S0e-bD
Assume no
motion
Find S
(simulated high
b-value)
High b-value
(b=2000 s/mm²)
DWI
(scan 2)
S=S0e-bD
Co-register
volumes
(estimating motion)
Rotate vector table
FIBER ORIENTATION ESTIMATION METHOD
Fiber/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
Average Number of Tracts in
Genu
120000
100000
80000
N Fibers
Genu
CSD
60000
40000
20000
Genu
Tensor
0
CSD
Tensor
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
DISTRIBUTED B0
SCANNING PARAMETERS
MOTION CORRECTION
No correction
Interpolation
Motion
Simulation