Detecting Subtle Changes in Structure

1
Detecting Subtle Changes in Structure

• Chris Rorden
• Voxel Based Morphometry
• Segmentation identifying gray and white matter
• Modulation- adjusting for normalizations spatial
  distortions.
• Diffusion Tensor Imaging
• Measuring white matter integrity
• Tractography and analysis.
• Many images are from Christian Gaser. You can see
  his presentations and get his VBM scripts from
  these sites
• fmri.uib.no/workshops/2006/mai/fmri/index.shtml
• dbm.neuro.uni-jena.de/home/

2
Voxel Based Morphometry

• Most lectures in course focus on functional MRI.
• However, anatomical scans can also help us infer
  brain function.
• Do people with chronic epilepsy show brain
  atrophy?
• Which brain regions atrophy with age?
• Do people with good spatial memory (taxi drivers)
  have different anatomy than other people?
• Voxel based morphometry is a tool to relate gray
  and white matter concentration with medical
  history and behavior

3
Morphometry

• Morphometry examines the shape, volume and
  integrity of structures.
• Classically, morphometry was conducted by
  manually segmenting a few regions of interest.
• Voxel based morphometry conducts an independent
  statistical comparison for each voxel in the
  brain.

Images from Christian Gaser
4
Voxel Based Morphometry

• VBM has some advantages over manual tracing
• Automated fast and not subject to individual
  bias.
• Able to examine regions that are not anatomically
  well defined.
• Able to see the whole brain
• Normalization compensates for overall differences
  in brain volume, which can add variance to manual
  tracing of un-normalized images.

5
VBM disadvantages

• VBM has clear disadvantages
• Crucially depends on accurate normalization.
• Low power gray matter random fields are very
  heterogenous (individual patterns of sulcal
  folding registration is always poor.
• Crucially depends on a priori probability maps.
• Assumes normal gray-white contrast. Focal
  Cortical Dysplasia
• Looks for differences in volume, can be disrupted
  if shape of brain is different problem for
  developmental disorders

6
Segmentation

• For segmentation, we start with a high quality
  MRI scan

7
Segmentation

• Scan is normalized
• Gray and white matter concentration is estimated

8
Partitioning Tissue Types

• VBM segments image into three tissue types gray
  matter, white matter and CSF.
• Typically done on T1 scans (best spatial
  resolution, good gray-white contrast).
• Only three tissue types will not cope with large
  lesions.
• Probability map each voxel has a 0..100 chance
  of being one of the 3 tissue types.

T1
white
gray
CSF
Images from Christian Gaser
9
Segmentation I Image Intensity
estimate for GM
p0.95
frequency
p0.05
Image intensity
WM
back-ground
GM
CSF
Images from Christian Gaser
10
Segmentation II Voxel location

• Maximization of a posteriori probability
  Bayesian approach (expectation maximization)
• Analogy
• We know that last year there were 248 of 365 days
  with rain in Norway (p0.68)
• the conditional (or posterior) probability for
  rain in Bergen will be pgt0.5

Images and text from Christian Gaser
11
Segmentation overview
Intensity based estimate for GM
Source Image
Final result
p0.05
p0.95
p0.90
p0.95
p0.05
p0.95
a priori GM map
12
Voxel Based Morphometry Steps
13
Homogeneity correction crucial

• Field inhomogeneity will disrupt intensity based
  segmentation.
• Bias correction required.

no correction
14
Normalization is crucial

• Poor normalization has two problems
• Image will not be registered with a priori map
  poor segmentation.
• Images from different people will not be
  registered we will compare different brain
  areas.
• Custom template and prior is useful
• Accounts for characteristics of your scanner.
• Accounts for characteristics of your population
  (e.g. age).
• Must be independent of your analysis
• Either formed from combination of both groups
  (controlexperimental) or from independent
  control group.

15
Two step segmentation
segmentation II
segmentation I
Step I Creation of customized template
segmentation II
segmentation I
norma- lization
averaging
Step II Optimized segmentation
customized template
norma- lization
MNI template
16
Image cleanup
segmented
17
Overview of Optimized VBM
T1
normalized
segmented II
smoothed
segmented I
masked
customized template
mask
18
VBM designs

• Longitudinal VBM
• Sensitive way to detect atrophy through time.
  Using the same individual reduces variability.
• Cross sectional studies
• Can compare two distinct populations
• Can also examine atrophy through time, though
  will require more people than longitudinal VBM.
• Most VBM studies use t-test (two group or
  timepoints), but correlational analysis also
  powerful.

19
SPM5 segmentation

• Unified segmentation
• Iterated steps of segmentation estimation, bias
  correction and warping
• Impact
• Warping of prior images during segmentation makes
  segmentation more independent from size,
  position, and shape of prior images
• much slower than SPM2

40 iterations segmentation
40 iterations bias correction
20 iterations warping
significant change of estimate
no significant change of estimate
20
Voxel Based Morphometry

• We can statistically analyze gray matter atrophy

Epilepsy
21
Segmentation Problem

• If someone has atrophy, normalization will
  stretch gray matter to make brain match healthy
  template.
• This will reduce ability to detect differences

Normalization will squish this region
Normalization will stretch this region
22
Image Modulation

• Analogy as we blow up a balloon, the surface
  becomes thinner. Likewise, as we expand a brain
  area its volume is reduced.

Without modulation
Source
Template
Modulated
23
Image Modulation

• Optimized Segmentation can adjust for distortions
  caused during normalization.
• Areas that had to be stretched are assumed to
  have less volume than areas that were compressed.
• Corrects for changes in volume induced by
  nonlinear normalization
• Multiplies voxel intensities by a modulation
  matrix derived from the normalization step
• Allows us to make inferences about volume,
  instead of concentration.

24
VBM and developmental syndromes

• Williams Syndrome
• Developmental syndrome Chromosome 7
• Manual Morphology shows
• 8-18 decrease in posterior GM/WM
• Most consistent finding is reduced intra-parietal
  sulcus depth and superior parietal lobe volume
  (see figure)
• Relatively preserved frontal GM/WM
• Creates unique shape
• Unique spatial distribution of gross volume loss
  influences VBM results depending on whether
  modulation is used

Control WS
Eckert et al. 2006b,c
25
Modulation and shape

• Shape differences influence modulated data.
• Deformation Based Morphometry can identify
  shape/gross volumetric differences.

Eckert et al., 2006a
26
Modulation is optional and controversial

• Modulation will smooth images, specificity will
  decrease
• Alternatively, you can covary overall brain
  volume by including GM or GMWM as nuisance
  regressor.

Example showing danger of modulation. This image
comes from an elderly participant, with
relatively large ventricles. Normalization
adjusts ventricle size, but the deformations are
spatially smooth, so tissue near the ventricles
(e.g. caudate) are also being spatially
compressed. Deformations exaggerated for
exposition
27
DBM (from Henson)

• Deformation-based Morphometry examines absolute
  displacements.
• E.G. Mean differences (mapping from an average
  female to male brain).

28
Cortical Thickness

• New methods can complement VBM.
• Freesurfers cortical thickness is powerful tool.
• Requires very good T1 scans.

29
VBM comments

• VBM findings are first step in understanding
  strucutural changes.
• Methods are a work in progress.
• www.tina-vision.net/docs/memos/2003-011.pdf
• Bookstein, 2001
• Davatzikos, 2004
• http//fmri.uib.no/workshops/2006/mai/fmri/index.s
  html
• Christian Gaser Markov Random Fields
  dbm.neuro.uni-jena.de/home/

30
Diffusion Weighted Imaging

• T1/T2 scans do not show acute injury. Diffusion
  weighted scans do.
• DW scans identify areas of permanent injury
• Measures random motion of water molecules.
• In ventricles, CSF is unconstrained, so high
  velocity diffusion
• In brain tissue, CSF more constrained, so less
  diffusion.

T2
DW
31
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).

32
DTI

• The diffusion tensor describes both the amount of
  diffusion, as well as the directions in which
  this diffusion is occurring.
• 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
  usual described by the term Fractional Anisotropy
  (FA).

MD differs
FA differs
33
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 V1V2V3 Football V1gtV2 V1gtV3 V3
V2 ??? V1gtV2gtV3
34
Diffusion Tensor Imaging

• To create a tensor, we need to collect collect
  multiple directions.
• Typically 12-16 directions.
• More directions offer a better estimate of
  optimal tensor.

35
DTI
DTI Tutorial
FA
Principle Tensor Vector

• MD

36
Tractography

• DTI can be used for tractography.
• This can identify whether pathways are abnormal.

Inferior frontal occipital tract
37
Kissing or crossing?

• Modelling each voxel as a tensor has limitations.
• Can not model fiber crossings.