DESCRIPTION

COMMAND:

  • ANTS

OPTIONS:

-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>: