Transcript DTI - Center for Advanced Brain Imaging

Detecting Subtle Changes in Structure
Chris Rorden
– Diffusion Tensor Imaging
Measuring white matter integrity
Tractography and analysis.
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Diffusion Weighted Imaging
 Diffusion weighted images measures random
motion of water molecules.
– In ventricles, CSF is unconstrained, so high velocity
diffusion
– In brain tissue, water more constrained, so less
diffusion.
DWI
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Diffusion Tensor Imaging (DTI)
 DTI is an extension of DWI that allows us to measure
direction of motion.
 DTI allows us to measure both the velocity and preferred
direction of diffusion
– In gray matter, diffusion is isotropic (similar in all directions)
– In white matter, diffusion is anisotropic (prefers motion along
fibers).
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DTI
 The amount of diffusion occurring in one pixel of a MR
image is termed the Apparent Diffusion Coefficient
(ADC) or Mean Diffusivity (MD).
 The non-uniformity of diffusion with direction is usually
described by the term Fractional Anisotropy (FA).
MD differs
FA differs
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Raw DTI data
DTI scans apply gradients of different
strengths (b-values) and directions (b-vectors).
These highlight different diffusion directions.
Simplest DTI is shown below: one b=0 image
(no directionality), plus six images with b=1000
but each with a different b-vector.
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Eddy current correction
 Gradients for DTI data cause spatial distortions.
 Different directions have different distortions.
 The B0 image has little distortion (it is a T2 weighted Image)
 Eddy current correction aligns all directions to the b0
(reference) image.
 Analogous to motion correction for fMRI data.
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What is a tensor?
 A tensor is composed of three
vectors.
– Think of a vector like an arrow
in 3D space – it points in a
direction and has a length.
 The first vector is the longest
– it points along the principle
axis.
 The second and third vectors
are orthogonal to the first.
Sphere:
V1=V2=V3
Football:
V1>V2
V1>V3
V3 = V2
???:
V1>V2>V3
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DTI
MD
FA
V1 modulated by FA
Color shows principle
tensor direction,
brightness shows FA
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The crossing fibers problem
Tensor (DTI) will have
trouble distinguishing
voxels with crossing
fibers from isotropic
regions.
Reality
Tensors
CSF (isotropic)
Crossing fibers
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Tractography
Programs like
medInria allow us to
measure integrity of
connections between
different regions.
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Statistics – MD and FA
The FA and MD maps from each individual can be
normalized to standard space.
Standard voxelwise statistics applied (like VBM)
Allows us to infer differences in white matter integrity
between groups.
Group mean images
based on normalized data.
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Statistics – tensors
You can measure fiber strength connectivity
brain regions and see if this differs between
groups.
Traditionally, tensor maps are very difficult to
normalize – we do not want to squish relative
shape of tensor.
– Recent advances are addressing this (DTITK)
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TBSS - Tract-Based Spatial Statistics
 TBSS is a FSL approach to
conduct between group
comparisons of DTI data.
 projects data onto group-mean
tract skeleton, allowing voxelwise
analysis
 addresses alignment problems
unsolved by nonlinear registration
 Overview
www.fmrib.ox.ac.uk/fsl/tbss/index.html
 Tutorial
www.fmrib.ox.ac.uk/fslcourse/lectures/practicals/fdt/index.htm
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Sample application
Young adults vary in their
ability to recollect
information.
TBSS shows that Fornix
integrity predicts this
variability (but not
variability in familiarity).
Rudebeck (2009) JoN
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