ANTsX/ANTsR

Name: ANTsR

Owner: Advanced Normalization Tools Ecosystem

Description: Advanced Normalization Tools in R

Created: 2012-09-12 16:28:03.0

Updated: 2018-01-18 07:55:04.0

Pushed: 2018-01-18 17:01:52.0

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README

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ANTsR

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

Description

Version: 0.4.0

License: Apache License 2.0

Depends: R (? 3.0), methods

Imports: Rcpp, tools, magrittr

LinkingTo: Rcpp, ITKR, ANTsRCore

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

Maintainer: Brian B. Avants

URL: homepage

BugReports: github issues

NeedsCompilation: yes

Travis checks: ANTsR results

Downloads

Reference manual: ANTsR.pdf

Vignettes:

Wiki: Notes and work in progress examples

Package source: from github

OS X Mavericks, Yosemite binaries: OSX

Linux binaries: Ubuntu

Windows installation option here

Install the binary, after downloading, via command line:

D INSTALL ANTsR_*.tgz
Research using ANTsR
Installation from source

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 will need to install R packages that ANTsR requires. Minimally: Install ITKR and ANTsRCore here and here then do:

ps <- c( "Rcpp", "RcppEigen", "tools", "methods", "magrittr", "rsvd" )
all.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: with devtools in R

ary( devtools )
stall_github("stnava/cmaker") # if you do not have cmake
all_github("stnava/ANTsR")

Method 2: from command line (most traditional method)

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

First, clone the repository:

t clone https://github.com/stnava/ITKR.git
t clone https://github.com/ANTsX/ANTsRCore.git
t clone https://github.com/ANTsX/ANTsR.git

Install the package as follows:

CMD INSTALL ITKR
CMD INSTALL ANTsRCore
CMD INSTALL ANTsR

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

Method 3: from binaries

Note that version numbers will change over time.

 https://github.com/stnava/ITKR/releases/download/latest/ITKR_0.4.12_R_x86_64-pc-linux-gnu.tar.gz
D INSTALL ITKR_0.4.12_R_x86_64-pc-linux-gnu.tar.gz
 https://github.com/ANTsX/ANTsRCore/releases/download/v0.4.2.1/ANTsRCore_0.4.2.1_R_x86_64-pc-linux-gnu.tar.gz
D INSTALL ANTsRCore_0.4.2.1_R_x86_64-pc-linux-gnu.tar.gz
 https://github.com/ANTsX/ANTsR/releases/download/latest/ANTsR_0.6_R_x86_64-pc-linux-gnu.tar.gz
D INSTALL ANTsR_0.6_R_x86_64-pc-linux-gnu.tar.gz

Method 4: platform independent via docker and kitematic

This is a beta operation that is in flux but may be convenient for some users.

Usage

Load the package:

ary(ANTsR)

List the available functions in the namespace ANTsR:

R::<double-tab>

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

Overview of ANTsR functionality and useful tools

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

ilename<-getANTsRData("mni")
-antsImageRead(mnifilename)
ImageWrite(img,mnifilename)
GetSpacing(img)
GetDirection(img)
GetOrigin(img)
t( img[50,60,44] )
t(max(img))

Index an image with a label

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

Convert a 4D image to a matrix

simg<-array( data=rnorm(125*10), dim=c(5,5,5,10))
simg<-as.antsImage(gaussimg)
t(dim(gaussimg))
<-getAverageOfTimeSeries( gaussimg )
lselect <- mask < 0
<-timeseries2matrix( gaussimg, voxelselect )
t(dim(gmat))

Convert a list of images to a matrix

ges<-100
t<-list()
( i in 1:nimages )

mimg<-makeImage( c(50,50) , rnorm(2500) )
mimg<-smoothImage(simimg,1.5)
ist[i]<-simimg

t a mask from the first image
<-getMask( ilist[[1]],
wThresh=mean(ilist[[1]]), cleanup=TRUE )
-imageListToMatrix( ilist, mask )
t(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.

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

etas<-makeImage( mask , mdli$beta.t[1,] )
ImageWrite( 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.

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

sume you ran the population example above
t<-sparseDecom( mat, mask, 0.2, 5, cthresh=2, its=2 )
<-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

th
esholdImage
ntifyCBF
sPreprocessfMRI
Perfusion
puteDVARS
ROIValues
odynamicRF
pectImageData3D
eGraph
rixToImages
sRegistration
tPrettyGraph
tBasicNetwork
TemplateCoordinates
sSet*

Parts of ImageMath from ANTs are accessible via

th

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

A good visualization alternative is antsSurf.

Direct access to ANTs tools

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:

R::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]" )

R::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]" )

Tagging a beta release

tag -d beta
push origin :refs/tags/beta
tag beta
push  --tags origin

Release notes

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.

0.4.0
0.3.3
0.3.2
0.3.1
0.3.0

First official release.

This work is supported by the National Institutes of Health's National Center for Advancing Translational Sciences, Grant Number U24TR002306. This work is solely the responsibility of the creators and does not necessarily represent the official views of the National Institutes of Health.