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H Jeremy BockholtRonald Pierson
Vincent MagnottaNancy C Andreasen
The BRAINS2 Morphometry The BRAINS2 Morphometry pipeline in action.pipeline in action.
2005 BRAINS2/Slicer Workshop
Reasons Structural AnalysisDepends 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 chemicalsUse 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 ResamplingT1 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.
CoregistrationAll 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 individuallyLandmarks used
Right-most extent of the brainLeft-most extent of the brainAnterior-most extent of the brainPosterior-most extent of the brainSuperior-most extent of the brain Inferior-most extent of the temporal lobeAC 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 BoxesCyan - Frontal
Blue - TemporalGreen -ParietalRed - Occipital
Pink - CerebellumYellow - Subcortical
Gray - BrainstemGray - 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 plugsKeep “pure” plugs - those with sufficiently low varianceK-means cluster the plugs to assign them to GM, WM, or CSFGenerate discriminant functions based on tissue assigned plugsApply 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 ImagesTissue classified image is coded on an 8 bit scale
Other = 0, Blood = 1Pure CSF = 10, Pure GM = 130, Pure WM = 250
Partial volume between CSF-GM and GM-WMDiscrete image generated from continuous image using the following formula.
CSF:10x70, GM: 70 < x 190WM: 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 VolumesGenerate 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 VolumesIn 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 AlgorithmUse 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 GMHelps 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 surfaceRepeat for other side
Algorithm Additions
CurvatureLook at current triangle wrt local neighborhood of triangles up to 3 triangles away
Determine if the current triangle is concave or convex
Cortical depthFollow 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 InterestSurface 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 ImagesResample/Coregister MR ImagesTissue ClassificationDefinition of BrainRegional Structure IdentificationVolumetric MeasurementsSurface Generation
Neural NetworkCurrently 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 LobesCerebellar 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 TracingTools 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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