ANTsR (version 0.4.0)

antsRegistration: Perform registration between two images.

Description

Register a pair of images either through the full or simplified interface to the ANTs registration method.

Usage

antsRegistration(fixed = NA, moving = NA, typeofTransform = "SyN",
  initialTransform = NA, outprefix = "", mask = NA, gradStep = 0.2,
  flowSigma = 3, totalSigma = 0, affMetric = "mattes", affSampling = 32,
  synMetric = "mattes", synSampling = 32, regIterations = c(40, 20, 0),
  verbose = FALSE, ...)

Arguments

fixed

fixed image to which we register the moving image.

moving

moving image to be mapped to fixed space.

typeofTransform

A linear or non-linear registration type. Mutual information metric by default. See Details.

initialTransform

transforms to prepend

outprefix

output will be named with this prefix.

mask

mask the registration.

gradStep

gradient step size (not for all tx)

flowSigma

smoothing for update field

totalSigma

smoothing for total field

affMetric

the metric for the affine part (GC, mattes, meansquares)

affSampling

the nbins or radius parameter for the syn metric

synMetric

the metric for the syn part (CC, mattes, meansquares, demons)

synSampling

the nbins or radius parameter for the syn metric

regIterations

vector of iterations for syn. we will set the smoothing and multi-resolution parameters based on the length of this vector.

verbose

request verbose output (useful for debugging)

...

additional options see antsRegistration in ANTs

Value

outputs a list containing:

• warpedmovout: Moving image warped to space of fixed image.

• warpedfixout: Fixed image warped to space of moving image.

• fwdtransforms: Transforms to move from moving to fixed image.

• invtransforms: Transforms to move from fixed to moving image.

Ouptut of 1 indicates failure

Details

typeofTransform can be one of:

• "Translation": Translation transformation.

• "Rigid": Rigid transformation: Only rotation and translation.

• "Similarity": Similarity transformation: scaling, rotation and translation.

• "QuickRigid": Rigid transformation: Only rotation and translation. May be useful for quick visualization fixes.'

• "DenseRigid": Rigid transformation: Only rotation and translation. Employs dense sampling during metric estimation.'

• "BOLDRigid": Rigid transformation: Parameters typical for BOLD to BOLD intrasubject registration'.'

• "Affine": Affine transformation: Rigid + scaling.

• "AffineFast": Fast version of Affine.

• "BOLDAffine": Affine transformation: Parameters typical for BOLD to BOLD intrasubject registration'.'

• "TRSAA": translation, rigid, similarity, affine (twice). please set regIterations if using this option. this would be used in cases where you want a really high quality affine mapping (perhaps with mask).

• "ElasticSyN": Symmetric normalization: Affine + deformable transformation, with mutual information as optimization metric and elastic regularization.

• "SyN": Symmetric normalization: Affine + deformable transformation, with mutual information as optimization metric.

• "SyNRA": Symmetric normalization: Rigid + Affine + deformable transformation, with mutual information as optimization metric.

• "SyNOnly": Symmetric normalization: no initial transformation, with mutual information as optimization metric. Assumes images are aligned by an inital transformation. Can be useful if you want to run an unmasked affine followed by masked deformable registration.

• "SyNCC": SyN, but with cross-correlation as the metric.

• "SyNabp": SyN optimized for abpBrainExtraction.

• "SyNBold": SyN, but optimized for registrations between BOLD and T1 images.

• "SyNBoldAff": SyN, but optimized for registrations between BOLD and T1 images, with additional affine step.

• "SyNAggro": SyN, but with more aggressive registration (fine-scale matching and more deformation). Takes more time than SyN.

• "TVMSQ": time-varying diffeomorphism with mean square metric

• "TVMSQC": time-varying diffeomorphism with mean square metric for very large deformation

Examples

# NOT RUN {
fi <- antsImageRead(getANTsRData("r16") )
mi <- antsImageRead(getANTsRData("r64") )
fi<-resampleImage(fi,c(60,60),1,0)
mi<-resampleImage(mi,c(60,60),1,0) # speed up
mytx <- antsRegistration(fixed=fi, moving=mi, typeofTransform = c('SyN') )
mywarpedimage <- antsApplyTransforms( fixed=fi, moving=mi,
  transformlist=mytx$fwdtransforms )

# }
# NOT RUN {
 # quick visualization fix for images with odd orientation
mni = antsImageRead( getANTsRData( "mni" ) )
strokt1=antsImageRead('strokt1.nii.gz')
strokt1reg=antsRegistration(
  fixed=mni,
  moving=strokt1,
  typeofTransform = "QuickRigid",verbose=TRUE )
 plot(  strokt1reg$warpedmovout, axis=3, nslices=20)
# now - how to use a mask
fi <- antsImageRead(getANTsRData("r16") )
fiseg = kmeansSegmentation( fi, 3 )
mi <- antsImageRead(getANTsRData("r64") )
msk = thresholdImage(fiseg$segmentation, 0, 0 )
mytx <- antsRegistration(fixed=fi, moving=mi, typeofTransform = c('SyNCC'),
  mask=msk, verbose=F )
jac = createJacobianDeterminantImage( fi, mytx$fwdtransforms[1] )
# }
# NOT RUN {
# }