Part of ants registration suite
ANTS
-x, --mask-image maskFileName
this mask -- defined in the 'fixed' image space defines the region of interest over which the registration is computed ==> above 0.1 means inside mask ==> continuous values in range [0.1,1.0] effect optimization like a probability. ==> values > 1 are treated as = 1.0 -m, --image-metric
The metric weights are relative to the weights on the N other metrics passed to ANTS --- N is unlimited. So, the weight, w_i on the i^{th} metric will be w_i=w_i/ ( sum_i w_i ).Intensity-Based Metrics:
CC/cross-correlation/CrossCorrelation[fixedImage,movingImage,weight,radius/OrForMI-#histogramBins] MI/mutual-information/MutualInformation[fixedImage,movingImage,weight,radius/OrForMI-#histogramBins] SMI/spatial-mutual-information/SpatialMutualInformation[fixedImage,movingImage,weight,radius/OrForMI-#histogramBins] PR/probabilistic/Probabilistic[fixedImage,movingImage,weight,radius/OrForMI-#histogramBins] SSD
--- standard intensity difference.[fixedImage,movingImage,weight,radius/OrForMI-#histogramBins]
MSQ/mean-squares/MeanSquares
-- demons-like, radius > 0 uses moving image gradient in metric
deriv.[fixedImage,movingImage,weight,radius/OrForMI-#histogramBins]
Point-Set-Based Metrics:
PSE/point-set-expectation/PointSetExpectation[fixedImage,movingImage,fixedPoints,movingPoints,weight,pointSetPercentage,pointSetSigma,boundaryPointsOnly,kNeighborhood,
PartialMatchingIterations=100000]
the partial matching option assumes the
complete labeling is in the first set of label parameters ... more iterations leads to more symmetry in the matching - 0 iterations means full asymmetry
[fixedImage,movingImage,fixedPoints,movingPoints,weight,pointSetPercentage,pointSetSigma,boundaryPointsOnly,kNeighborhood,alpha,meshResolution,splineOrder,numberOfLevels,useAnisotropicCovariances] -o, --output-naming
The name for the output - a prefix or a name+type : e.g. -o OUT or -o OUT.nii or -o OUT.mha --R
TODO/FIXME: the --R sets an ROI option -- it passes a vector of parameters that sets the center and bounding box
of the region of interest for a sub-field
registration. e.g. in 3D the option setting -r 10x12x15x50x50x25
sets up a
bounding box of size 50,50,25 with origin at 10,12,15 in voxel (should this be physical?) coordinates. <VALUES>: 0 -i, --number-of-iterations
number of iterations per level -- a 'vector' e.g. : 100x100x20 <VALUES>: 10x10x5 --Restrict-Deformation
restrict the gradient that drives the deformation by scalar factors along specified dimensions -- a TReal 'vector' of length ImageDimension to multiply against the similarity metric's gradient values --- e.g. in 3D : 0.1x1x0 --- will set the z gradient to zero and scale the x gradient by 0.1 and y by 1 (no change). Thus, you get a 2.5-Dimensional registration as there is still 3D continuity in the mapping. <VALUES>: 1x1 -v, --verbose
verbose output --use-all-metrics-for-convergence
enable to use weighted sum of all metric terms for convergence computation. By default, only the first metric is used <VALUES>: 0 -h
Print the help menu (short version). <VALUES>: 0 --help
Print the help menu. <VALUES>: 1, 0 -t, --transformation-model
TRANSFORMATION[gradient-step-length,number-of-time-steps,DeltaTime,symmetry-type].
Choose one of the following TRANSFORMATIONS:
Diff = diffeomorphic Elast =
Elastic
Exp = exponential diff
Greedy Exp = greedy exponential diff, like
diffeomorphic demons. same parameters.
SyN -- symmetric normalization
DeltaTime is the integration time-discretization step - sub-voxel - n-time steps currently fixed at 2 <VALUES>: SyN[0.5] -r, --regularization
REGULARIZATION[gradient-field-sigma,def-field-sigma,truncation].
Choose one of
the following REGULARIZATIONS:
Gauss = gaussian DMFFD = directly manipulated
free form deformation <VALUES>: Gauss[3,0.5] -a, --initial-affine
use the input file as the initial affine parameter -F, --fixed-image-initial-affine
use the input file as the initial affine parameter for the fixed image --fixed-image-initial-affine-ref-image
reference space for using the input file as the initial affine parameter for the fixed image -T, --geodesic
= 0 / 1 / 2, 0 = not time-dependent, 1 = asymmetric , 2 = symmetric -G, --go-faster
true / false -- if true, SyN is faster but loses some accuracy wrt inverse-identity constraint, see Avants MIA 2008. <VALUES>: false --continue-affine
true (default) | false, do (not) perform affine given the initial affine parameters <VALUES>: true --number-of-affine-iterations
number of iterations per level -- a 'vector' e.g. : 100x100x20 <VALUES>: 10000x10000x10000 --use-NN
use nearest neighbor interpolation <VALUES>: 0 --use-Histogram-Matching
use histogram matching of moving to fixed image <VALUES>: 0 --affine-metric-type
MI: mutual information (default), MSQ: mean square error, SSD, CC: Normalized correlation, CCH: Histogram-based correlation coefficient (not recommended), GD: gradient difference (not recommended) <VALUES>: MI --MI-option
option of mutual information: MI_bins x MI_samples (default: 32x32000) <VALUES>: 32x5000 --rigid-affine
use rigid transformation : true / false(default) <VALUES>: false --do-rigid
use rigid transformation : true / false(default) <VALUES>: false --affine-gradient-descent-option
option of gradient descent in affine transformation: maximum_step_length x relaxation_factor x minimum_step_length x translation_scales <VALUES>: 0.1x0.5x1.e-4x1.e-4 --use-rotation-header
use rotation matrix in image headers: true (default) / false <VALUES>: false --ignore-void-origin
ignore the apparently unmatched origins (when use-rotation-header is false and the rotation matrix is identity: true (default) / false <VALUES>: false --gaussian-smoothing-sigmas
At each resolution level the image is subsampled and smoothed by Gaussian convolution. This option allows the user to override the default smoothing by specifying sigma values (in mm) for smoothing both fixed and moving images for each resolution level. <VALUES>: --subsampling-factors
At each resolution level the image is subsampled and smoothed by Gaussian convolution. This option allows the user to override the default subsampling by specifying the subsampling factor for both fixed and moving images for each resolution level. <VALUES>: