part of ANTS registration suite
CreateDTICohort implements the work of Van Hecke et al. (On the construction of
a ground truth framework for evaluating voxl-based diffusion tensor MRI analysis
methods, Neuroimage 46:692-707, 2009) to create simulated DTI data sets. The
only difference is that all registrations (both for the input population and for
the output population) are assumed to take place outside of this program.
-d, --image-dimensionality 2/3
- This option forces the image to be treated as a specified-dimensional image. If
not specified, the program tries to infer the dimensionality from the input
-a, --dti-atlas inputDTIAtlasFileName
- A diffusion tensor atlas image is required input for creating the cohort.
- -x, --label-mask-image maskImageFileName
A mask image can be specified which determines the region(s). to which the
simulated pathology operations are applied. See also the option '--pathology'.
If no mask is specified one is created by thresholding the atlas FA map at 0.2
unless a lower threshold is specified.
-n, --noise-sigma <noiseSigma=18>
- This parameter characterizes the Rician noise in the original DWIimages. Van
Hecke uses the noise-estimation method of Sijbers et al. "Automatic estimation
of the noise variance from the histogram of a magnetic resonance image", Phys.
Med. Biol. 52:1335-1348, 2007.
-p, --pathology label[<percentageChangeEig1=-0.05>,<percentageChangeAvgEig2andEig3=0.05>,<numberOfVoxels=all or percentageOfvoxels>]
- The user can specify the simulated pathology in a given area using a label mask.
If no label is prepended to parameters, the specified parameters are applied to
all labels.Pathology is simulated by changing the eigenvalues. Typically this
involves a decrease in the largest eigenvalue and an increase in the average of
the remaining eigenvalues. Change is specified as a percentage of the current
eigenvalues. However, care is taken to ensure that diffusion direction does not
change. Additionally, one can specify the number of voxels affected in each
region or one can specify the percentage of voxels affected. Default is to
change all voxels. Note that the percentages must be specified in the range
[0,1]. For dimension=3 where the average transverse diffusion eigenvalues are
altered, this change is propagated to the distinct eigenvalues by forcing the
ratio to be the same before the change.
- -w, --dwi-parameters [B0Image,directionFile,bvalue]
This option specifies the parameters of the output diffusion-weighted images
including the directions and b-values. The directions are specified using a
direction file which has as its first line the number of directions.Each
successive three lines contains the x, y, and z directions, respectively, and a
single b-value. Note that several direction files of this format are distributed
with the Camino DTI toolkit
Alternatively, one can specify a scheme file where each direction is specified
followed by a b-value for that direction, i.e. <x1> <y1> <z1> <bvalue1> ...
-r, --registered-population textFileWithFileNames.txt
- If one wants to introduce inter-subject variabilitya registered DTI population
to the DTI atlas is required. This variability is modeled by a PCA decomposition
on a combination of the first eigenvalue image and the average of the second and
third eigenvalues.The registered image file names are specified using a text
file where each line is the name of an individual DTI.
-o, --output [outputDirectory,fileNameSeriesRootName,<numberOfControls=10>,<numberOfExperimentals=10>]
- The output consists of a set of diffusion-weighted images for each subject. Each
file name is prepended with the word 'Control' or 'Experimental'. The number of
control and experimental subjects can be also be specified on the command line.
Default is 10 for each group.
- Print the help menu (short version).
- Print the help menu.
<VALUES>: 1, 0