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