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									The BRAINS2 Morphometry
    pipeline in action.

       H Jeremy Bockholt
         Ronald Pierson
        Vincent Magnotta
       Nancy C Andreasen




      2005 BRAINS2/Slicer Workshop
    Reasons Structural Analysis
Depends on the question that you are asking -
 Volumetric Analysis: How big is it? What
 kind of tissue is there and how much of it?
 Morphometric Analysis: What is the size and
 shape of the brain or of its structures?
 Other types – DTI and Spectroscopy: What
 other static characteristics can we measure?
 White matter direction and coherence, and
 concentrations of biologically significant
 chemicals
 Use of ROIs for functional image analysis
Basic Goals of Standard Workup:

Volumetric Analysis: Measure volumes of
tissue in gross regions of the brain
Automate the process to make it possible to
handle large volumes of scans
Remove or minimize effects of different
raters and rater fatigue or drift
Create a set of images that will be useful for
future work – measurement of other structures
via manual tracing, etc.
Standard Workup Overview

Acquire MR Images
Resample/Coregister MR Images
Tissue Classification
Neural Network Structure Identification
Measure Volumes
Surface Generation
Surface Measurements
        Image Acquisition

Each site acquires T1 and either T2 or PD.
Iowa acquires single NEX=2 T1 and Nex=3
T2.
Other 3 sites acquire multiple NEX=1 scans.
QA review at each site before uploading to
SRB and after downloading at Iowa prior to
processing.
           Manual Resampling
T1 images are realigned in a standard orientation.
The standard orientation calls for lining up the interhemispheric
fissure. This sets the alignment in the coronal and axial planes.
In addition, the anterior commisure and posterior commisure are
used for the horizontal orientation in the sagittal plane.
               Coregistration
All other images are coregistered to the manually
reoriented T1 by use of AIR or Mutual Information
coregistration.
For those sites acquiring multiple NEX=1 scans,
after coregistration all of the scans in each modality
are averaged together to produce an image with
better CNR.
When fitting is complete each image is resampled
to a new orientation and a resolution of 0.5 mm
cubic voxels.
Point to point correspondence with any given set of
coordinates referring to the same point in all of the
images
        Talairach Bounds
Define a Talairach-based atlas for the each
scan individually
Landmarks used
 Right-most extent of the brain
 Left-most extent of the brain
 Anterior-most extent of the brain
 Posterior-most extent of the brain
 Superior-most extent of the brain
 Inferior-most extent of the temporal lobe
 AC and PC locations
                          Talairach Atlas
                              Talairach atlas coordinate system




Resampled image with overlaid Talairach coordinate system
      Talairach Regions
Talairach Atlas warped onto current brain.
Various "boxes" assigned to various regions
Measure volumes of labelled brain regions

                         Talairach Boxes
                             Cyan - Frontal
                            Blue - Temporal
                            Green -Parietal
                            Red - Occipital
                           Pink - Cerebellum
                          Yellow - Subcortical
                           Gray - Brainstem
How do we know what type of tissue
         each voxel is?
Tissue characteristics in a scan are determined
by sampling for three possible classifications
– gray matter, white matter and CSF. Blood
is traced.
Using these “training classes”, create a set of
rules to classify each voxel in the image.
Multiple modalities used, makes it possible to
define the edge of the surface CSF.
   Tissue Classification

Randomly choose 2x2x2 mm plugs
Keep “pure” plugs - those with sufficiently
low variance
K-means cluster the plugs to assign them to
GM, WM, or CSF
Generate discriminant functions based on
tissue assigned plugs
Apply discriminant functions to the entire
image
     Tissue Classification
The basis for all subsequent steps in standard
workup
 Neural network structure identification
 Cortical surface generation
 Image normalization and enhancement
Defines the tissue type at each voxel in the
image
 Continuous classification - Multiple tissue types
 per voxel
 Discrete classification - Single tissue type per voxel
T1 and T2
 Images


 Tissue
Classified
 Images
      Classified Images
Tissue classified image is coded on an 8 bit
scale
 Other = 0, Blood = 1
 Pure CSF = 10, Pure GM = 130, Pure WM = 250
Partial volume between CSF-GM and GM-
WM
Discrete image generated from continuous
image using the following formula.
 CSF:10x70,
 GM: 70 < x 190
 WM: 190<x 250
Definition of the "BRAIN"
Artificial Neural Network used to define
"Brain"
 Trained from manual traces
 Uses a standard, 3 layer, fully connected neural
 network
 Trained using back-propagation
Inputs
 Signal intensity within a spherical region of the voxel
 Probability information
 Spatial location information
                ROI Editing

Most regional
  cutouts are
  reliable before
  editing
Output of neural
  network
  trimmed for
  validity
  Tissue-Classified Volumes
Generate measures both for continuous
and discrete images
 In general, discrete data has been used
Regional measures partitioned into GM,
WM, CSF, blood and other.
Measurements made for total and
internal CSF
 Can compute surface CSF based on these
 results
Measurements corrected for signal
inhomogeniety
  Tissue-Classified Volumes
In each region the volumes are measured
for GM, WM, CSF, blood and other
(unclassified)
Frontal, temporal, parietal and occipital
lobes
Subcortical, cerebellum and brainstem
Ventricles
Add and subtract variables to create
measures of interest
Surface Generation Algorithm
  Use these ROIs to define masks which
  represent exclusion regions for surface
  generation – “the surface can’t go here.”
  Use a marching cubes type algorithm (Wyvill)
  to define the 130 isosurface in the image.
   Parametric center of GM
   Helps avoids the buried cortex problem
  Limit search space to side of interest
   Start out on the correct side of the hemisphere
   traces
  Keep the largest connected surface
  Repeat for other side
    Algorithm Additions

Curvature
 Look at current triangle wrt local neighborhood of
 triangles up to 3 triangles away
 Determine if the current triangle is concave or
 convex
Cortical depth
 Follow normal from center of triangle as well as
 each vertex
 Find shortest distance to 190 value (WM border)
Cortical Surface
 Surface Measurements

Many, Many, Many variables ............
Measurements of Interest
 Surface Area (mm2): Gyral, Fundal, Total
 Curvature: Gyral or Fundal
 Thickness (mm): Gyral, Fundal, Total
Measures obtained by Talairach boxes as
well
 BE CAREFUL USING REGIONAL
 MEASURES
 Standard Workup Complete
Acquire MR Images
Resample/Coregister MR Images
Tissue Classification
Definition of Brain
Regional Structure Identification
Volumetric Measurements
Surface Generation
             Neural Network
Currently defines the following regions
 Caudate
 Putamen
 Thalamus
 Cerebellum
 Cerebellar lobes (warping)
 Hippocampus (requires editing)
 Globus Pallidus (requires editing)
In the near future will use a warped method for all
structures – more valid, less editing
Will also add nucleus accumbens and amygdala
Neural
Network
 Inputs
Artifical Neural Networks
             Cerebellum Lobes
Cerebellar Lobe Volumes: Uses landmark-based warp for
semiautomated measurement of Lobes I through V (anterior
lobe), Lobe VI and Crus I of VIIA (superior posterior lobe),
Crus II of VIIA through Lobe X (inferior posterior lobe), and the
central white matter and output nuclei(corpus medullare).
         Manual Tracing
Tools provided in BRAINS2 facilitate accurate
tracing using multiple images and views.
Useful for accurate placement of ROIs for DTI,
functional image analysis.
Can create spheres, cubes around a point
Convert to code image – warp, coregister, import
into SPM, etc.
Parcellation of cortical surface
    Future Methods Available

Create rigorously valid cortical lobe
definitions by warping a template brain to
individual’s scan.
Other high-dimensional, non-linear warp
projects to analyze shape
FreeSurfer – semiautomated cortical
parcellation
Automated Regional Measures

Talairach Atlas – the space which the brain
occupies is broken up into boxes, and each
box is labeled with what region it belongs to.
Create an atlas for each scan (based on the
Talairach atlas) that does a good job of
defining brain regions
Also need a way to define what is brain and
what is not
       Talairach Atlas II
What about the cerebellum?
 Not included in Talairach Atlas
 We have added two additional boxes to the
 inferior aspect of the Talairach atlas to include
 the cerebellum
Used for automated gross regional
measures
Provides a coordinate system for structure
probability

								
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