Rxnat Paper Case Scenario
Adi Gherman
2020-06-22
All code for this document is located at here.
In this tutorial I will discuss performing tissue class segmentation using the FAST function from FSL and it’s wrapper function in fslr, fast_nobias (Muschelli et al. 2015).
Data Packages
To perform this analysis, I will select one subject from the IXI neuroimaging dataset hosted on https://nitrc.org/ (Kennedy et al. 2016). The Rxnat package is required to be installed in order to access the NITRC image repository as well as an account with NITRC: new account request
packages = installed.packages()
packages = packages[, "Package"]
if (!"Rxnat" %in% packages) {
source("https://neuroconductor.org/neurocLite.R")
neuroc_install("Rxnat")
}Loading Data
Please visit the general Rxnat tutorial on how to setup the Rxnat environment variables. Next we’ll read and reorient the T1 image using the readrpi function from the fslr package.
library(Rxnat)
nitrc <- xnat_connect("https://nitrc.org/ir", xnat_name="NITRC")
# Download the subject T1 weighted image
file_path <- nitrc$download_dir(
experiment_ID = 'NITRC_IR_E10464',
scan_type = "T1",
extract = TRUE)
t1_fname <- file_path[1]
t1 <- readrpi(t1_fname)
ortho2(t1, add.orient = TRUE)T1 Weighted Image
Remove neck and drop empty dimensions
The neck removal step is implemented using the remove_neck function from the extrantsr package. The empty image dimensions (including the neck slices) can be dropped by using the function dropEmptyImageDimensions from the neurobase package.
noneck = remove_neck(file_path,
template.file = fslr::mni_fname(brain = TRUE, mm = 1),
template.mask = fslr::mni_fname(mm = 1, brain = TRUE, mask = TRUE),
verbose = FALSE
)
red = dropEmptyImageDimensions(noneck)
red <- readrpi(red)
ortho2(red, add.orient = TRUE)Inhomogeneity correction
To correct the bias field signal we use the bias_correct function from the extrantsr package, which uses the N4 inhomogeneity correction.
t1_n4 = bias_correct(red,
correction = "N4",
outfile = tempfile(fileext = ".nii.gz"), retimg = FALSE
)
t1_n4 <- readrpi(t1_n4)Malf registration
Once images are bias field corrected, we apply brain extraction using a form of multi-atlas label fusion (MALF). MALF uses a collection of previously labeled brain images (atlases), aligns the T1-weighted image to each atlas, and obtains a labeled T1-weighted image for each registration. This approach is implemented using the malf function from the malf.templates package, which includes the templates from the 2012 MICCAI Multi-Atlas Labeling Challenge.
timgs = mass_images(n_templates = 35)
ss = malf(infile = t1_n4,
template.images = timgs$images,
template.structs = timgs$masks,
keep_images = FALSE,
verbose = FALSE)Perform skull stripping
To do this we use the preprocess_mri_within function from the extrantsr package. This function performs N4 bias correction, image registration (if multi- sequence data is given), skull stripping (estimating the brain mask if one is not supplied), and brain mask application to the registered images.
proc_outfile <- paste0("T1_Processed.nii.gz")
proc_outfile <- file.path(tempdir(),proc_outfile)
skull_ss <- preprocess_mri_within(
files = t1_n4,
outfiles = proc_outfile,
correction = "N4",
maskfile = ss,
correct_after_mask = FALSE)
t1_ss <- readrpi(proc_outfile)
ortho2(red,
t1_ss,
col.y=alpha("red", 0.3),
add.orient = TRUE)Perform WhiteStripe intensity normalization
WhiteStripe intensity normalization can be implemented using the whitestripe andwhitestripe_norm functions from the WhiteStripe package.
ind = whitestripe(img = t1_ss, type = "T1", stripped = TRUE)$whitestripe.indGetting T1 Modes
Quantile T1 VOIws_t1 = whitestripe_norm(t1_ss, ind)Perform segmentation
This is implemented here using the FAST function from FSL. FAST segments a 3D brain image into different tissue types. We will use the fast_nobias from the fslr package, which assumes that the the bias field was removed.
ss_tcs = fslr::fast_nobias(ws_t1,
verbose = TRUE)
double_ortho(ws_t1,
ss_tcs,
add.orient=TRUE)Session Info
devtools::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.0.0 (2020-04-24)
os macOS Catalina 10.15.5
system x86_64, darwin17.0
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/New_York
date 2020-06-23
─ Packages ───────────────────────────────────────────────────────────────────
package * version date lib
abind 1.4-5 2016-07-21 [1]
ANTsR 0.5.6.0.0 2020-05-14 [1]
ANTsRCore 0.7.4 2020-05-13 [1]
assertthat 0.2.1 2019-03-21 [2]
backports 1.1.7 2020-05-13 [2]
bitops 1.0-6 2013-08-17 [2]
callr 3.4.3 2020-03-28 [2]
cli 2.0.2 2020-02-28 [2]
codetools 0.2-16 2018-12-24 [2]
colorspace 1.4-1 2019-03-18 [1]
crayon 1.3.4 2017-09-16 [2]
curl 4.3 2020-05-31 [1]
desc 1.2.0 2018-05-01 [2]
devtools 2.3.0 2020-04-10 [2]
digest 0.6.25 2020-02-23 [2]
dplyr * 1.0.0 2020-05-29 [1]
ellipsis 0.3.1 2020-05-15 [2]
evaluate 0.14 2019-05-28 [2]
extrantsr * 3.9.13 2020-05-19 [1]
fansi 0.4.1 2020-01-08 [2]
farver 2.0.3 2020-01-16 [1]
fs 1.4.1 2020-04-04 [2]
fslr * 2.24.1 2019-08-05 [1]
generics 0.0.2 2018-11-29 [1]
glue 1.4.1 2020-05-13 [2]
highr 0.8 2019-03-20 [2]
htmltools 0.4.0 2019-10-04 [2]
httr 1.4.1 2019-08-05 [2]
ITKR 0.5.3 2020-05-12 [1]
knitr 1.28 2020-02-06 [1]
lattice 0.20-41 2020-04-02 [2]
lifecycle 0.2.0 2020-03-06 [2]
magrittr 1.5 2014-11-22 [2]
malf.templates * 1.2.0 2020-04-29 [1]
Matrix 1.2-18 2019-11-27 [2]
matrixStats 0.56.0 2020-03-13 [1]
memoise 1.1.0 2017-04-21 [2]
mgcv 1.8-31 2019-11-09 [2]
munsell 0.5.0 2018-06-12 [1]
neurobase * 1.29.0 2020-01-14 [1]
nlme 3.1-147 2020-04-13 [2]
oro.nifti * 0.10.1 2020-05-18 [1]
pillar 1.4.4 2020-05-05 [2]
pkgbuild 1.0.8 2020-05-07 [2]
pkgconfig 2.0.3 2019-09-22 [2]
pkgload 1.0.2 2018-10-29 [2]
plyr 1.8.6 2020-03-03 [1]
prettyunits 1.1.1 2020-01-24 [2]
processx 3.4.2 2020-02-09 [2]
ps 1.3.3 2020-05-08 [2]
purrr 0.3.4 2020-04-17 [2]
R.matlab 3.6.2 2018-09-27 [1]
R.methodsS3 1.8.0 2020-02-14 [1]
R.oo 1.23.0 2019-11-03 [1]
R.utils 2.9.2 2019-12-08 [1]
R6 2.4.1 2019-11-12 [2]
Rcpp 1.0.4.6 2020-04-09 [2]
RcppEigen 0.3.3.7.0 2019-11-16 [1]
RCurl 1.98-1.2 2020-04-18 [2]
remotes 2.1.1 2020-02-15 [2]
rlang 0.4.6 2020-05-02 [2]
rmarkdown 2.1 2020-01-20 [1]
RNifti 1.1.0 2020-01-31 [1]
rprojroot 1.3-2 2018-01-03 [2]
Rxnat * 1.0.9 2020-06-04 [1]
scales * 1.1.1 2020-05-11 [1]
sessioninfo 1.1.1 2018-11-05 [2]
stapler 0.7.1 2020-01-09 [1]
stringi 1.4.6 2020-02-17 [2]
stringr 1.4.0 2019-02-10 [1]
testthat 2.3.2 2020-03-02 [1]
tibble 3.0.1 2020-04-20 [1]
tidyselect 1.1.0 2020-05-11 [2]
usethis 1.6.1 2020-04-29 [2]
vctrs 0.3.0 2020-05-11 [2]
WhiteStripe * 2.3.2 2020-05-01 [1]
withr 2.2.0 2020-04-20 [2]
xfun 0.13 2020-04-13 [2]
yaml 2.2.1 2020-02-01 [2]
source
CRAN (R 4.0.0)
Neuroconductor-Devel-Releases (R 4.0.0)
Neuroconductor-Devel-Releases (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
local
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
Neuroconductor-Devel-Releases (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
Neuroconductor-Devel-Releases (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
Neuroconductor-Devel-Releases (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
Neuroconductor-Releases (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
Github (adigherman/Rxnat@a3c0522)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
Neuroconductor-Devel-Releases (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
[1] /Users/adi/Library/R/4.0/library
[2] /Library/Frameworks/R.framework/Versions/4.0/Resources/libraryReferences
Kennedy, David N, Christian Haselgrove, Jon Riehl, Nina Preuss, and Robert Buccigrossi. 2016. “The NITRC Image Repository.” NeuroImage 124. Elsevier: 1069–73.
Muschelli, John, Elizabeth Sweeney, Martin Lindquist, and Ciprian Crainiceanu. 2015. “Fslr: Connecting the Fsl Software with R.” The R Journal 7 (1): 163–75.