Figure 3. Two-year old 5066-004-01_10_T1_labels_EMS White Matter ImageLeft: Average cortical thickness on the white matter along the boundary. Right: Cortical Thickness values computed by the algorithm (scattered points) on the white matter.
A b-spline basis for unbiased atlas building, a technique released by Polina Golland and Serdar Balci at
MIT, was used to build the atlas. Group-wise registration is one of the b-spline atlas building tools that
take input as a list of image files and gives out put as b-spline atlas images.
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4.1.2.1) Input and Output files
There are two parameter files specified in the b-spline atlas building. The first is a text file that
specifies a list of input files and an input and output directory. The ten input images are T1 skull-
stripped images. The second file is a text file that takes parameters, which will be explained in sub-
section 3.3.
The following is an example of skullfilename.txt:
#
# The path of the input folder for images.# All images are assumed to be in the same folder.# (don’t forget to have slash as the last character)## If images are in different folders ignore# this parameter and supply the full pathname# as filename.#i /home/sci/ehan/nworkspace/Research/CortThickData/skullstrip/
## The path of the output folder.# All outputs are saved to this folder##o /home/sci/ehan/nworkspace/Research/CortThickData/computeOutputs/
# names of the input files# if inputFolder is specified, the pathname is relative to that# folder. Otherwise supply the full pathname#f 506600401_10_T1_labels_EMS_skullstrip.gipl.gzf 509000302_10_T1_labels_EMS_skullstrip.gipl.gzf 506600402_10_T1_labels_EMS_skullstrip.gipl.gzf 513200501_10_T1_labels_EMS_skullstrip.gipl.gzf 508400301_10_T1_labels_EMS_skullstrip.gipl.gzf 513200502_10_T1_labels_EMS_skullstrip.gipl.gzf 508400302_10_T1_labels_EMS_skullstrip.gipl.gzf 515800401_10_T1_labels_EMS_skullstrip.gipl.gzf 509000301_10_T1_labels_EMS_skullstrip.gipl.gzf 515800402_10_T1_labels_EMS_skullstrip.gipl.gz
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The following are figures of input images:
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Figure 6. Input of ten skull-stripped images for group-wise registration.Input files are: 1) 506600401_10_T1_labels_EMS_skullstrip.gipl.gz2) 506600402_10_T1_labels_EMS_skullstrip.gipl.gz3) 508400301_10_T1_labels_EMS_skullstrip.gipl.gz4) 508400302_10_T1_labels_EMS_skullstrip.gipl.gz5) 509000301_10_T1_labels_EMS_skullstrip.gipl.gz6) 509000302_10_T1_labels_EMS_skullstrip.gipl.gz7) 513200501_10_T1_labels_EMS_skullstrip.gipl.gz8) 513200502_10_T1_labels_EMS_skullstrip.gipl.gz9) 515800401_10_T1_labels_EMS_skullstrip.gipl.gz10)515800402_10_T1_labels_EMS_skullstrip.gipl.gz
4.1.2.2) Parameters
The second text file takes lists of parameters. The useBspline option should be enabled.
The useBsplineHigh option should be disabled initially. This option controls whether the b-spline
control points are refined after the initial b-spline registration. Users can experiment with this option,
but it does not always run reliably. The three numberOfSpatialSamples options control the
percentage of the image used in computing the image match term. Increasing this value improves the
stability of the optimization, but requires more computation time. If the objective function does not
decrease consistently try increasing this term. The useNormailizeFilter should be disabled
because the intensities of the images should be standardized prior to running this algorithm.
Using Affine Alignment to Template
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If users wish to use an affine alignment for each image from previous registration to a template
they should create in the output directory the directory tree Affine/TransformFiles. In this
directory there should be a file for each input image specified in the skullfilenames.txt file.
The file should end in a .txt extension and should contain an itk AffineTransform in double
precision.
The following is an example of parameters.txt:
## PARAMETERS OF BINARY FILE### metricType specifies which objective function to use# possible options:## entropy: congealing with entropy# variance: registering to the mean template image using# sum of square differencesmetricType variance## If useBpline is set off, only affine registration is done,# if it is on and useBspline high is off, bspline registration# is done with specified grid region. If useBsplineHigh is on# bspline registration with mesh refinement is performed## Options to use:# useBspline on/off# useBsplineHigh on/off#useBspline onuseBsplineHigh off## defines the initial bspline grid size along each dimension#bsplineInitialGridSize 8## When using Bspline grid refinement, this options defines# how many number of refinements to use. After each level# number Bspline control points are doubled (8>16>32)#numberOfBsplineLevel 2## All objective functions make use of stochastic subsampline
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# following options define the number of spatial samples as# percentage of the total number of voxels in the image (Try# to increase the number of samples if the registration# accuracy is poor)#numberOfSpatialSamplesAffinePercentage 0.050numberOfSpatialSamplesBsplinePercentage 0.1numberOfSpatialSamplesBsplineHighPercentage 0.2## following options define number of multiresolution levels# used in optimization if set to one no multiresolution# optimization is performed. Affine/Bspline/BsplineHigh# define number of multiresolution levels used for each# registration stage## (For high resolution anatomical images at 256x256x128 voxels# we used 3 levels, decrease the number of levels if the# resolution of the input image is low )multiLevelAffine 2multiLevelBspline 2multiLevelBsplineHigh 2## Following options define the number iterations to be# performed Optimization is terminated after a fixed number# of iterations no other termination options are used#optAffineNumberOfIterations 50optBsplineNumberOfIterations 40optBsplineHighNumberOfIterations 40## Following options define the learning rate of the# optimizers for each stage of the registration## (decrease the learning rate if you get# "all samples mapped outside" error#optAffineLearningRate 1e10optBsplineLearningRate 1e6optBsplineHighLearningrate 1e7## Currently there are three optimizer types. "gradient" is# a fixed step gradient descent search. "lineSearch" is# gradient descent search where step size is determined# using line search.## optimizerType gradient/lineSearch/SPSA
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#optimizerType lineSearch## Specifies the percentage increase in the sampling rate# after each multiresolution level#affineMultiScaleSamplePercentageIncrease 4.0bsplineMultiScaleSamplePercentageIncrease 4.0## Specifies increase in the number of iterations# after each resolution level#affineMultiScaleMaximumIterationIncrease 2.0bsplineMultiScaleMaximumIterationIncrease 2.0## Specifies optimizer step length increase after each# multiresolution level#affineMultiScaleStepLengthIncrease 4.0bsplineMultiScaleStepLengthIncrease 4.0## the width of the parzen window to compute the entropy# used by all metric types computing entropy#parzenWindowStandardDeviation 10.0## Use normalize filter to normalize input images# to have mean zero and standard deviation 1.#useNormalizeFilter off## Write 3D images to file.# turn off to save disk space#write3DImages on############################################## ADVANCED OPTIONS############################################### the level of registration to be started# Use this option if you want to start the registration# using the results of a previous registration## 0 (default): no initialization, all registrations are
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# performed# 1 : Affine parameters are read from the file# 2 : Bspline parameters are read from file (initial size should# match the transform from file )#StartLevel 0## Uses a mask on the images. Only pixels inside the mask are# considered during the registration possible options mask# none/single/all none: do not use mask single: only use# mask for the first image all: use mask for all images##mask all## specifies the mask type# possible options:# maskype connectedThreshold/neighborhoodConnected## connectedThreshold: adds all pixels# to the mask if its value is smaller# than threshold1 than add connected# pixels whose value is smaller than# threshold2# neighborhoodConnected: same as connectedThreshold but a# pixel is added only if it is all# connected within a radius of one##maskType connectedThreshold#threshold1 0#threshold2 1## specifies the translation scale coefficients with respect# to the affine coefficients smaller values mean larger step# size along translation directions 1/scale is used!translationScaleCoeffs 1e4## Maximum number of iterations performed for a line search# if the optimizer is lineSearch#maximumLineIteration 6BSplineRegularizationFlag ongaussianFilterKernelWidth 5
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4.1.2.3) Running GroupwiseRegistration
The command for running GroupwiseRregistration is as follows:
Figure 8. Transformed images of distance map on white matter and grey matter (two year). Left images are average distance map on a grey matter and white matter. Right images are scatter distance map on a grey matter and white matter.Output files are:1) 506600401_10_T1_labels_EMS DistanceMapAverageOnGreyScalarTransform.mha 2) 506600401_10_T1_labels_EMSDistanceMapOnGreyScalarTransform.mha
Figure 9. Transformed images of distance map on white matter and grey matter (four year). Left images are average distance map on a grey matter and white matter. Right images are scatter distance map on a grey matter and white matter.
To visualize cortical thicknesses in 3D, we want to create 3D volume using MeshCortThick.
MeshCortThick takes two inputs with the i option. The first input is a white mater or grey
matter mask of the atlas created from ImageMath, and the second input is a cortical thickness along
the boundary created with AtCorthThick. This will create 3 outputs such as a mesh file
(*.meta), a text file (*.txt), and a vtk file (*.vtk). The mesh file contains volume information
and the text file contains scalar cortical thicknesses at each mesh points.
The usage of MeshCortThick is as follows:usage: MeshCortThick i <ImageToMeshFileName> <ThicknessFileName>or MeshCortThick m <MeshFileName> <ThicknessFileName>
1) White Matter:>MeshCortThick i <ImageToMeshFileName: White Matter mask of the atlas created from the ImageMath (section 4.1.8)> <ThicknessFileName: Cortical Thickness along the White Matter boundary created with AtCorthThick>
2) Grey Matter:>MeshCortThick i <ImageToMeshFileName: Grey Matter mask of the atlas created from ImageMath (section 4.1.8)> <ThicknessFileName: Cortical Thickness along the Grey Matter boundary created with AtCorthThick>
4.1.11) 3D Cortical Thickness Visualization ( KWMeshVisuUNC)
Once 3D mesh cortical thickness is created, we want to visualize the cortical thickness map on white matter and grey matter with a tool KWMeshVisuUNC. There are two steps required to render the 3D cortical thickness. The first, load a mesh file (*.meta) that created from MeshCortThick and the second, load a 1D attribute file (*.txt) that created from MeshCortThick.
The command is as follows:/usr/sci/projects/neuro/software_kraken/bin/./KWMeshVisuUNC &
The following are results of 3D cortical thickness on the grey matter and white matter:
Figure 15. 3D Visualization of cortical thickness on the grey matter.
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Figure 16. 3D Visualization of cortical thickness on the white matter.
Figure 15. 3D Visualization of cortical thickness on the grey matter and white matter.
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References
[1] GOODLETT, C. DTI Atlas Building User Guide (Jan. 2009).
