A package providing ANTs features in R as well as imaging-specific data representations, spatially regularized dimensionality reduction and segmentation tools.

Version: 0.3.2

License: GPL (>=2)

Depends: R (≥ 3.0), methods

Imports: Rcpp, tools, magrittr

LinkingTo: Rcpp, ITKR

Author: Brian B. Avants, Benjamin M. Kandel, Jeff T. Duda, Philip A. Cook, Nicholas J. Tustison

Maintainer: Brian B. Avants

URL: homepage

BugReports: github issues

NeedsCompilation: yes

Travis checks: ANTsR results

Reference manual: ANTsR.pdf

Vignettes:

• ANTsR

• iMath

Wiki: Notes and work in progress examples

Package source: from github

OS X Mavericks, Yosemite binaries: OSX

Linux binaries: Ubuntu

We are working toward Windows binaries.

Install the binary, after downloading, via command line:

R CMD INSTALL ANTsR_*.tgz

• Inter-modality inference yet to be added RIPMMARC

• Eigenanatomy for multiple modality population studies function sparseDecom

• Tumor segmentation function mrvnrfs (not exactly the same but close)

• Multiple modality pediatric template and population study employs several aspects of ANTsR

• Structural networks from subject-level data function makeGraph plus yet to be added RIPMMARC

• SCCAN relating neuroimaging and cognitive batteries function sparseDecom2

• Sparse regression with manifold smoothness constraints function sparseRegression

• Prior-based eigenanatomy function sparseDecom

Please read this entire section before choosing which method you prefer.

In general, these assume you have git installed / accessible in your environment, as well as a compiler, preferably clang. you may also need cmake if you do/can not install cmaker.

Windows users should see Rtools and maybe, also, installr for assistance in setting up their environment for building (must have a compiler too). To my knowledge, there are no recorded instances of ANTsR being installed on Windows. If someone does so, we would like to know.

You may need to install R packages that ANTsR requires. For example, minimally:

mydeps <- c( "Rcpp", "tools", "methods", "magrittr" )
install.packages( pkgs = mydeps, dependencies = TRUE )

You can gain additional functionality by installing packages that are listed in the DESCRIPTION file under Suggests. A complete list of recommended ancillary packages here.

Method 1: drat See full instructions here but briefly:

install.packages("drat")
drat::addRepo("ANTs-R")
install.packages("ANTsR")

Thanks to zarquon42b.

Method 2: with devtools in R

library( devtools )
# install_github("stnava/cmaker") # if you do not have cmake
install_github("stnava/ITKR")
install_github("stnava/ANTsR")

Method 3: from command line (most traditional method)

Assumes git, cmake and compilers are available in your environment (as above).

First, clone the repository:

$ git clone https://github.com/stnava/ITKR.git
$ git clone https://github.com/stnava/ANTsR.git

Install the package as follows:

$ R CMD INSTALL ITKR
$ R CMD INSTALL ANTsR

The travis.yml file also shows a way to install from Linux command line.

Load the package:

library(ANTsR)

List the available functions in the namespace ANTsR:

ANTsR::<double-tab>

Call help on a function via ?functionName or see function arguments via args(functionName)

If nothing else, ANTsR makes it easy to read and write medical images and to map them into a format compatible with R.

Read, write, access an image

mnifilename<-getANTsRData("mni")
img<-antsImageRead(mnifilename)
antsImageWrite(img,mnifilename)
antsGetSpacing(img)
antsGetDirection(img)
antsGetOrigin(img)
print( img[50,60,44] )
print(max(img))

Index an image with a label

gaussimg<-array( data=rnorm(125), dim=c(5,5,5))
arrayimg<-array( data=(1:125), dim=c(5,5,5))
img<-as.antsImage( arrayimg )
print( max(img) )
print( mean(img[ img > 50  ]))
print( max(img[ img >= 50 & img <= 99  ]))
print( mean( gaussimg[ img >= 50 & img <= 99  ]) )

Convert a 4D image to a matrix

gaussimg<-array( data=rnorm(125*10), dim=c(5,5,5,10))
gaussimg<-as.antsImage(gaussimg)
print(dim(gaussimg))
mask<-getAverageOfTimeSeries( gaussimg )
voxelselect <- mask < 0
mask[ voxelselect  ]<-0
mask[ !voxelselect  ]<-1
gmat<-timeseries2matrix( gaussimg, mask )
print(dim(gmat))

Convert a list of images to a matrix

nimages<-100
ilist<-list()
for ( i in 1:nimages )
{
  simimg<-makeImage( c(50,50) , rnorm(2500) )
  simimg<-smoothImage(simimg,1.5)
  ilist[i]<-simimg
}
# get a mask from the first image
mask<-getMask( ilist[[1]],
  lowThresh=mean(ilist[[1]]), cleanup=TRUE )
mat<-imageListToMatrix( ilist, mask )
print(dim(mat))

Do fast statistics on a big matrix

Once we have a matrix representation of our population, we might run a quick voxel-wise regression within the mask.
Then we look at some summary statistics.

mat<-imageListToMatrix( ilist, mask )
age<-rnorm( nrow(mat) ) # simulated age
gender<-rep( c("F","M"), nrow(mat)/2 ) # simulated gender
# this creates "real" but noisy effects to detect
mat<-mat*(age^2+rnorm(nrow(mat)))
mdl<-lm( mat ~ age + gender )
mdli<-bigLMStats( mdl, 1.e-4 )
print(names(mdli))
print(rownames(mdli$beta.t))
print(paste("age",min(p.adjust(mdli$beta.pval[1,]))))
print(paste("gen",min(p.adjust(mdli$beta.pval[2,]))))

Write out a statistical map

We might also write out the images so that we can save them for later or look at them with other software.

agebetas<-makeImage( mask , mdli$beta.t[1,] )
antsImageWrite( agebetas, tempfile(fileext ='.nii.gz') )

Neighborhood operations

Images neighborhoods contain rich shape and texture information.
We can extract neighborhoods for further analysis at a given scale.

mnit<-getANTsRData("mni")
mnit<-antsImageRead(mnit)
mnit <- resampleImage( mnit , rep(4, mnit@dimension) )
mask2<-getMask(mnit,lowThresh=mean(mnit),cleanup=TRUE)
radius <- rep(2,mnit@dimension)
mat2<-getNeighborhoodInMask(mnit, mask2, radius,
  physical.coordinates = FALSE,
  boundary.condition = "mean" )

The boundary.condition says how to treat data that is outside of the mask or the image boundaries. Here, we replace this data with the mean in-mask value of the local neighborhood.

Eigenanatomy & SCCAN

Images often have many voxels ($p$-voxels) and, in medical applications, this means that $p>n$ or even $p>>n$, where $n$ is the number of subjects. Therefore, we often want to "intelligently" reduce the dimensionality of the data. However, we want to retain spatial locality. This is the point of "eigenanatomy" which is a variation of sparse PCA that uses (optionally) biologically-motivated smoothness, locality or sparsity constraints.

# assume you ran the population example above
eanat<-sparseDecom( mat, mask, 0.2, 5, cthresh=2, its=2 )
eseg<-eigSeg(mask,eanat$eig,F)

The parameters for the example above are set for fast processing. You can see our paper for some theory on these methods[@Kandel2014a].

More information is available within the examples that can be seen within the help for sparseDecom, sparseDecom2 and the helper function initializeEigenanatomy. You might also see the sccan tutorial.

Other useful tools

?iMath
?ThresholdImage
?quantifyCBF
?antsPreprocessfMRI
?aslPerfusion
?computeDVARS
?getROIValues
?hemodynamicRF
?inspectImageData3D
?makeGraph
?matrixToImages
?antsRegistration
?plotPrettyGraph
?plotBasicNetwork
?getTemplateCoordinates
?antsSet*

Parts of ImageMath from ANTs are accessible via

?iMath

for more fMRI focused tools, see RKRNS and its github site github RKRNS.

A good visualization alternative is antsSurf.

Alternatively, one can use any function in the namespace by providing arguments exactly same as one provides to the corresponding command-line version.

For example, to call the antsRegistration routine:

ANTsR::antsRegistration( "-d", "2", "-m", "mi[r16slice.nii.gz,r64slice.nii.gz,1,20,Regular,0.05]", "-t", "affine[1.0]", "-c", "2100x1200x1200x0", "-s", "3x2x1x0", "-f", "4x3x2x1","-u", "1", "-o", "[xtest,xtest.nii.gz,xtest_inv.nii.gz]" )

ANTsR::antsRegistration( "-d", "2", "-m", "mi[r16slice.nii.gz,r64slice.nii.gz,1,20,Regular,0.05]", "-t", "affine[1.0]", "-c", "2100x1200x1200x0", "-s", "3x2x1x0", "-f", "4x3x2x1", "-m", "cc[r16slice.nii.gz,r64slice.nii.gz,1,4]", "-t", "syn[5.0,3,0.0]", "-i", "100x100x0", "-s", "2x1x0", "-f", "3x2x1", "-u", "1", "-o", "[xtest,xtest.nii.gz,xtest_inv.nii.gz]" )

More like development highlights, as opposed to release notes. See git log for the complete history. We try to follow these versioning recommendations for R packages. Under these guidelines, only major.minor is an official release.

• COMP: test for DVCL define requirement to improve clang and gcc compilations

• WIP: transform objects can be created on the fly and manipulated thanks to jeff duda

• ENH: automation for eigenanatomy

• ENH: reworked SCCAN and eanat

• ENH: mrvnrfs

• ENH: resting state Vignette

• DOC: clarify/extend antsApplyTransforms

• ENH: multidimensional images

• STYLE: iMath not ImageMath in ANTsR

• WIP: iMath improvements

• WIP: ASL pipeline fuctionality

• BUG: Fixed image indexing bug

• BUG: plot.antsImage improvements

• ENH: more antsRegistration options

• ENH: geoSeg

• ENH: JointLabelFusion and JointIntensityFusion

• ENH: Enable negating images

• ENH: weingarten curvature

• ENH: antsApplyTransformsToPoints with example

• ENH: renormalizeProbabilityImages

• ENH: Suppress output from imageWrite.

First official release.