README.md
In Jfortin1/RAVEL: Removal of Artificial Voxel Effect by Linear Regression
RAVEL 
Intensity normalization methods for structural MRIs
Creator: Jean-Philippe Fortin, fortin946@gmail.com
Authors: Jean-Philippe Fortin, John Muschelli
License:
GPL-2
Software status
| Resource: | Travis CI |
| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------- |
| Platform: | OSX |
| R CMD check | 
|
References
| Method | Citation | Paper Link |
| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------- |
| RAVEL | Jean-Philippe Fortin, Elizabeth M Sweeney, John Muschelli, Ciprian M Crainiceanu, Russell T Shinohara, Alzheimer’s Disease Neuroimaging Initiative, et al. Removing inter-subject technical variability in magnetic resonance imaging studies. NeuroImage, 132:198–212, 2016. | Link |
| WhiteStripe | Russell T Shinohara, Elizabeth M Sweeney, Jeff Goldsmith, Navid Shiee, Farrah J Mateen, Peter A Calabresi, Samson Jarso, Dzung L Pham, Daniel S Reich, Ciprian M Crainiceanu, Australian Imaging Biomarkers Lifestyle Flagship Study of Ageing, and Alzheimer’s Disease Neuroimaging Initiative. Statistical normalization techniques for magnetic resonance imaging. Neuroimage Clin, 6:9–19, 2014. | Link |
Table of content
1. Introduction
RAVEL is an R package that combines the preprocessing and statistical
analysis of magnetic resonance imaging (MRI) datasets within one
framework. Users can start with raw images in the NIfTI format, and end
up with a variety of statistical results associated with voxels and
regions of interest (ROI) in the brain. RAVEL stands for Removal of
Artificial Voxel Effect by Linear regression, the main preprocessing
function of the package that allows an effective removal of between-scan
unwanted variation. We have shown in a recent
paper
that RAVEL improves significantly population-wide statistical inference.
RAVEL is now part of the Neuroconductor
project.
Installation
The following dependencies need to be installed first:
install.packages("devtools")
devtools::install_github("muschellij2/neurobase")
devtools::install_github("muschellij2/WhiteStripe")
RAVEL can be installed using the following command:
devtools::install_github("jfortin1/RAVEL")
2. Image preprocessing
We present a pre-normalization preprocessing pipeline implemented in the
R software, from raw images to images ready for intensity normalization
and statistical analysis. Once the images are preprocessed, users can
apply their favorite intensity normalization and the scan-effect
correction tool RAVEL as presented in Section 1 above. We present a
preprocessing pipeline that uses the R packages ANTsR and fslr.
While we have chosen to use a specific template space (JHU-MNI-ss), a
specific registration (non-linear diffeomorphic registration) and a
specific tissue segmentation (FSL FAST), users can choose other
algorithms prior to intensity normalization and in order for RAVEL to
work. The only requirement is that the images are registered to the same
template space.
2.1. Prelude
To preprocess the images, we use the packages fslr and ANTsR. The
package fslr is available on CRAN, and requires FSL to be installed on
your machine; see the FSL
website for installation. For
ANTsR, we recommend to install the latest stable version available at
the ANTsR GitHub page. The
version used for this vignette was ANTsR_0.3.2.tgz. For the template
space, we use the JHU-MNI-ss atlas (see Section 1.2) included in the
EveTemplate package, available on GitHub at
https://github.com/Jfortin1/EveTemplate. For data examples, we use 4
T1-w scans from the package RAVELData available on GitHub at
https://github.com/Jfortin1/RAVELData. Once the packages are properly
installed, we are ready to start our preprocessing of T1-w images. We
first load the packages into R:
library(fslr)
library(ANTsR)
library(RAVELData)
library(EveTemplate)
have.fsl() # Should be TRUE if fsl is correctly installed
and let’s specify the path for the different files that we will need:
# JHU-MNI-ss template:
library(EveTemplate)
template_path <- getEvePath("T1")
# JHU-MNI-ss template brain mask:
template_brain_mask_path <- getEvePath("Brain_Mask")
# Example of T1-w MPRAGE image
scan_path <- system.file(package="RAVELData", "extdata/scan1.nii.gz")
2.2. JHU-MNI-ss template (_EVE_ atlas)
2.3. Registration to template
Tp perform a non-linear registration to the JHU-MNI-ss template, one can
use the diffeomorphism algorithm via the ANTsR package. Note that we
perform the registration with the skulls on. Here is an example where we
register the scan1 from the RAVELData package to the JHU-MNI-ss
template:
library(ANTsRCore)
library(ANTsR)
template <- antsImageRead(template_path, 3)
scan <- antsImageRead(scan_path,3)
outprefix <- gsub(".nii.gz","",scan_path) # Prefix for the output files
output <- antsRegistration(fixed = template, moving = scan, typeofTransform = "SyN", outprefix = outprefix)
scan_reg <- antsImageClone(output$warpedmovout) # Registered brain
The object scan_reg contains the scan registed to the template. Note
that the object is in the ANTsR format. Since I prefer to work with
the oro.nifti package, which is compatible with flsr, I convert the
object to a nifti object using the function ants2oro as follows:
# devtools::install_github("muschellij2/extrantsr")
# or
# source("https://neuroconductor.org/sites/default/files/neurocLite.R")
# neuro_install("extrantsr")
library(extrantsr)
scan_reg <- extrantsr::ants2oro(scan_reg)
We can save the registered brain in the NIfTi format using the
writeNIfTI command:
writeNIfTI(scan_reg, "scan_reg")
Since scan_reg is converted to a nifti object, we can use the
function ortho2 from the fslr package to visualize the scan:
ortho2(scan_reg, crosshairs=FALSE, mfrow=c(1,3), add.orient=FALSE)
2.4. Intensity inhomogeneity correction
We perform intensity inhomogeneity correction on the registered scan
using the N4 Correction from the ANTsR package:
scan_reg <- extrantsr::oro2ants(scan_reg) # Convert to ANTsR object
scan_reg_n4 <- n4BiasFieldCorrection(scan_reg)
scan_reg_n4 <- extrantsr::ants2oro(scan_reg_n4) # Conversion to nifti object for further processing
2.5. Skull stripping
template_brain_mask <- readNIfTI(template_brain_mask_path, reorient=FALSE)
scan_reg_n4_brain <- niftiarr(scan_reg_n4, scan_reg_n4*template_brain_mask)
ortho2(scan_reg_n4_brain, crosshairs=FALSE, mfrow=c(1,3), add.orient=FALSE)
2.6. Tissue Segmentation
There are different tissue segmentation algorithms available in R. My
favorite is the FSL FAST segmentation via the
fslr
package. Let’s produce the tissue segmentation for the
scan_reg_n4_brain scan
above:
ortho2(scan_reg_n4_brain, crosshairs=FALSE, mfrow=c(1,3), add.orient=FALSE, ylim=c(0,400))
The last line of code produces via the ortho2 function from the fslr
package the following visualization of the template:
We perform a 3-class tissue segmentation on the T1-w image with the FAST
segmentation
algorithm:
scan_reg_n4_brain_seg <- fast(scan_reg_n4_brain, verbose=FALSE, opts="-t 1 -n 3")
ortho2(scan_reg_n4_brain_seg, crosshairs=FALSE, mfrow=c(1,3), add.orient=FALSE)
The object scan_reg_n4_brain_seg is an image that contains the
segmentation labels 0,1,2 and 3 referring to Background, CSF, GM and
WM voxels respectively.
2.7. Creation of a tissue mask
Suppose we want to create a mask for CSF.
scan_reg_n4_brain_csf_mask <- scan_reg_n4_brain_seg
scan_reg_n4_brain_csf_mask[scan_reg_n4_brain_csf_mask!=1] <- 0
ortho2(scan_reg_n4_brain_csf_mask, crosshairs=FALSE, mfrow=c(1,3), add.orient=FALSE)
We use the fact that the file scan_reg_n4_brain_seg is equal to 1 for
CSF, 2 for GM and 3 for WM. For instance, a WM mask could be created as
follows:
scan_reg_n4_brain_wm_mask <- scan_reg_n4_brain_seg
scan_reg_n4_brain_wm_mask[scan_reg_n4_brain_wm_mask!=3] <- 0
ortho2(scan_reg_n4_brain_wm_mask, crosshairs=FALSE, mfrow=c(1,3), add.orient=FALSE)
3. Intensity normalization and RAVEL correction
Since MRI intensities are acquired in arbitrary units, image intensities
are not comparable across scans, between subjects and across sites.
Intensity normalization (or intensity standardization) is paramount
before performing between-subject intensity comparisons. The RAVEL
package includes the popular histogram matching normalization
(normalizeHM) as well as the White Stripe normalization
(normalizeWS); see the table below for the reference papers. Once the
images intensities are normalized, the RAVEL correction tool can be
applied using the function normalizeRAVEL to remove additional
unwanted variation using a control region. Because we have found that
the combination White Stripe + RAVEL was best at removing unwanted
variation, the function normalizeRAVEL performs White Stripe
normalization by default prior to the RAVEL correction.
Note: registration is also called spatial normalization which is
unrelated to intensity
normalization.
Available methods
| Function | Method | Modalities supported at the moment | Paper Link |
| ---------------- | ------------------ | ---------------------------------- | -------------------------------------------------------------------------- |
| normalizeRaw | No normalization | T1, T2, FLAIR, PD | |
| normalizeRAVEL | RAVEL | T1, T2, FLAIR | Link |
| normalizeWS | White Stripe | T1, T2, FLAIR | Link |
| normalizeHM | Histogram Matching | T1, T2, FLAIR, PD | Link |
| normalizeZScore| Z-score transformation | T1, T2, FLAIR, PD | |
Briefly, each function takes as input a list of NIfTI file paths
specifying the images to be normalized, and return a matrix of
normalized intensities where rows are voxels and columns are scans. We
note that the input files must be the files associated with preprocessed
images registered to a common template. The different functions are
described below.
3.1 No normalization
The function normalizeRaw takes as input the preprocessed and
registered images, and create a matrix of voxel intensities without
intensity normalization. For conventional MRI images, we recommend to
apply an intensity normalization to the images (see normalizeWS or
normalizeRAVEL). The main purpose of the function normalizeRaw is
for exploration data analysis (EDA), methods development and methods
comparison.
| Argument | Description | Default |
| -------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | ------- |
| input.files | vector or list of the paths for the input NIfTI image files to be normalized | |
| output.files | Optionnal vector or list of the paths for the output images. By default, will be the input.files with "_RAW" appended at the end. | NULL |
| brain.mask | NIfTI image path for the binary brain mask. Must have value 1 for the brain and 0 otherwise | |
| returnMatrix | Should the matrix of normalized images be returned? Rows correspond to voxels specified by brain.mask, and columns correspond to scans. | TRUE |
| writeToDisk | Should the normalized images be saved to the disk as NIfTI files? | FALSE |
| verbose | Should the function be verbose? | TRUE |
3.2 White Stripe normalization
The function normalizeWS takes as input the preprocessed and
registered images, applies the White Stripe normalization algorithm to
each image separately via the WhiteStripe R package, and creates a
matrix of normalized voxel intensities. Note that the White Stripe
normalization is also included as a first step in the RAVEL algorithm
implemented in the normalizeRAVEL
function.
| Argument | Description | Default |
| ------------------ | ----------------------------------------------------------------------------------------------------------------------------------------- | ------- |
| input.files | vector or list of the paths for the input NIfTI image files to be normalized | |
| output.files | Optionnal vector or list of the paths for the output images. By default, will be the input.files with "_WS" appended at the end. | NULL |
| brain.mask | NIfTI image path for the binary brain mask. Must have value 1 for the brain and 0 otherwise | |
| WhiteStripe_Type | What is the type of images to be normalized? Must be one of “T1”, “T2” and “FLAIR”. | T1 |
| returnMatrix | Should the matrix of normalized images be returned? Rows correspond to voxels specified by brain.mask, and columns correspond to scans. | TRUE |
| writeToDisk | Should the normalized images be saved to the disk as NIfTI files? | FALSE |
| verbose | Should the function be verbose? | TRUE |
3.3 Histogram matching normalization
The function normalizeHM takes as input the preprocessed and
registered images, applies the Histogram matching normalization algorithm to
each image separately, and creates a
matrix of normalized voxel intensities.
| Argument | Description | Default |
| ------------------ | ----------------------------------------------------------------------------------------------------------------------------------------- | ------- |
| input.files | vector or list of the paths for the input NIfTI image files to be normalized | |
| output.files | Optionnal vector or list of the paths for the output images. By default, will be the input.files with "_WS" appended at the end. | NULL |
| brain.mask | NIfTI image path for the binary brain mask. Must have value 1 for the brain and 0 otherwise | |
| type | What is the type of images to be normalized? Must be one of “T1”, “T2”, "PD", and “FLAIR”. | T1 |
| returnMatrix | Should the matrix of normalized images be returned? Rows correspond to voxels specified by brain.mask, and columns correspond to scans. | TRUE |
| writeToDisk | Should the normalized images be saved to the disk as NIfTI files? | FALSE |
| verbose | Should the function be verbose? | TRUE |
3.4 Z-score transformation (whole-brain)
The function normalizeZScore takes as input the preprocessed and
registered images, applies a whole-brain z-score transformation to
each image separately, and creates a
matrix of normalized voxel intensities.
| Argument | Description | Default |
| ------------------ | ----------------------------------------------------------------------------------------------------------------------------------------- | ------- |
| input.files | vector or list of the paths for the input NIfTI image files to be normalized | |
| output.files | Optionnal vector or list of the paths for the output images. By default, will be the input.files with "_WS" appended at the end. | NULL |
| brain.mask | NIfTI image path for the binary brain mask. Must have value 1 for the brain and 0 otherwise | |
| type | What is the type of images to be normalized? Must be one of “T1”, “T2”, "PD", and “FLAIR”. | T1 |
| returnMatrix | Should the matrix of normalized images be returned? Rows correspond to voxels specified by brain.mask, and columns correspond to scans. | TRUE |
| writeToDisk | Should the normalized images be saved to the disk as NIfTI files? | FALSE |
| verbose | Should the function be verbose? | TRUE |
3.5 RAVEL normalization
The function normalizeRAVEL takes as input the preprocessed and
registered images, and a control region mask, and applies the RAVEL
correction method to create a matrix of normalized voxel intensities.
The White Stripe normalization is included by default as a first step in
the RAVEL algorithm. The next section explains how to create a control
region
mask.
| Argument | Description | Default |
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------- |
| input.files | vector or list of the paths for the input NIfTI image files to be normalized | |
| output.files | Optionnal vector or list of the paths for the output images. By default, will be the input.files with "_RAVEL" appended at the end. | NULL |
| brain.mask | NIfTI image path for the binary brain mask. Must have value 1 for the brain and 0 otherwise | |
| control.mask | NIfTI image path for the binary control region mask. Must have value 1 for the control region and 0 otherwise. See the helper function mask_intersect for the creation of a control.mask. | |
| mod | Model matrix for outcome of interest and other covariates | NULL |
| WhiteStripe | Should White Stripe normalization be performed before RAVEL? | TRUE |
| WhiteStripe_Type | If WhiteStripe is TRUE, what is the type of images to be normalized? Must be one of “T1”, “T2” and “FLAIR”. | T1 |
| k | Integer specifying the number of principal components to be included in the RAVEL correction. | 1 |
| returnMatrix | Should the matrix of normalized images be returned? Rows correspond to voxels specified by brain.mask, and columns correspond to scans. | TRUE |
| writeToDisk | Should the normalized images be saved to the disk as NIfTI files? | FALSE |
| verbose | Should the function be verbose? | TRUE |
3.6 Creation of a control region for RAVEL
RAVEL uses a control region of the brain to infer unwanted variation
across subjects. The control region is made of voxels known to be
not associated with the phenotype of interest. For instance, it is
known that CSF intensities on T1-w images are not associated with the
progression of AD. The control region must be specified in the argument
control.mask of the function normalizeRAVEL as a path to a NIfTI
file storing a binary mask. In the case of a CSF control region, one way
to create such a binary mask is to create a CSF binary mask for each
scan first, and then to take the intersection of all those binary masks. This
can be done with the function maskIntersect in RAVEL. The function takes as
input a list of binary masks (either nifti objects or a list of NIfTI
file paths), and will output the intersection of all the binary masks.
By default, the function will save the intersection mask to the disk as
a NIfTI file, as specified by
output.file:
Example:
dir <- file.path(find.package("RAVELData"), "extdata")
masks <- list.files(dir, full.names=TRUE, pattern="*mask*.nii*")
intersect_mask <- maskIntersect(masks, output.file=tempfile())
When the number of subjects is large, the intersection mask may be
empty, as a consequence of anatomical variation between subjects. As a
solution, the function maskIntersect has the option to create an
intersection mask that is less stringent by requiring the control region
to be present in only a given percentage of the subjects, using the
option prob. By default, prob is equal to 1, meaning 100% of the
subjects has the final voxels labelled as CSF. For instance, to require
that the final control region is shared for at least 90% of the
subjects, one would
type
intersect_mask <- maskIntersect(masks, output.file=tempfile(), prob=0.9)
For studies with a small number of subjects, the opposite problem may
arise: too many voxels labelled as CSF, close to the skull, might be
retained in the final intersection mask. Mask erosion, for instance
using fslr, may be performed to
remove such voxels and refine the control mask.
3.7 Controlling for biological covariates in RAVEL
In removing the unwanted variation estimated using control voxels, it is possible to preserve biological variation by specifying biological covariates in the normalizeRAVEL function, similar to ComBat harmonization.
For instance, suppose we want to normalize intensities across participants using CSF, and also want to make sure that we don't remove variation in intensities associated with age. We first need to build a model matrix for the biological covariates (here age):
age <- c(70,62,43,76) #Simulated age
gender <- c("M", "M", "F", "F")
mod <- model.matrix(~age+gender)
Note that while the model matrix has an intercept column, this will be automatically handled internally by the normalizeRAVEL function. To run RAVEL while adjusting for age and gender, we include mod as an argument as follows:
Y.ravel.mod <- normalizeRAVEL(input.files=input.files,
control.mask=control.mask,
brain.mask=brain.mask,
k=1,
mod=mod,
returnMatrix=TRUE,
WhiteStripe=FALSE
)
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RAVEL
Intensity normalization methods for structural MRIs
Creator: Jean-Philippe Fortin, fortin946@gmail.com
Authors: Jean-Philippe Fortin, John Muschelli
License: GPL-2
Software status
| Resource: | Travis CI |
| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------- |
| Platform: | OSX |
| R CMD check | |
References
| Method | Citation | Paper Link | | ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------- | | RAVEL | Jean-Philippe Fortin, Elizabeth M Sweeney, John Muschelli, Ciprian M Crainiceanu, Russell T Shinohara, Alzheimer’s Disease Neuroimaging Initiative, et al. Removing inter-subject technical variability in magnetic resonance imaging studies. NeuroImage, 132:198–212, 2016. | Link | | WhiteStripe | Russell T Shinohara, Elizabeth M Sweeney, Jeff Goldsmith, Navid Shiee, Farrah J Mateen, Peter A Calabresi, Samson Jarso, Dzung L Pham, Daniel S Reich, Ciprian M Crainiceanu, Australian Imaging Biomarkers Lifestyle Flagship Study of Ageing, and Alzheimer’s Disease Neuroimaging Initiative. Statistical normalization techniques for magnetic resonance imaging. Neuroimage Clin, 6:9–19, 2014. | Link |
Table of content
1. Introduction
RAVEL is an R package that combines the preprocessing and statistical analysis of magnetic resonance imaging (MRI) datasets within one framework. Users can start with raw images in the NIfTI format, and end up with a variety of statistical results associated with voxels and regions of interest (ROI) in the brain. RAVEL stands for Removal of Artificial Voxel Effect by Linear regression, the main preprocessing function of the package that allows an effective removal of between-scan unwanted variation. We have shown in a recent paper that RAVEL improves significantly population-wide statistical inference. RAVEL is now part of the Neuroconductor project.
Installation
The following dependencies need to be installed first:
install.packages("devtools")
devtools::install_github("muschellij2/neurobase")
devtools::install_github("muschellij2/WhiteStripe")
RAVEL can be installed using the following command:
devtools::install_github("jfortin1/RAVEL")
2. Image preprocessing
We present a pre-normalization preprocessing pipeline implemented in the
R software, from raw images to images ready for intensity normalization
and statistical analysis. Once the images are preprocessed, users can
apply their favorite intensity normalization and the scan-effect
correction tool RAVEL as presented in Section 1 above. We present a
preprocessing pipeline that uses the R packages ANTsR and fslr.
While we have chosen to use a specific template space (JHU-MNI-ss), a
specific registration (non-linear diffeomorphic registration) and a
specific tissue segmentation (FSL FAST), users can choose other
algorithms prior to intensity normalization and in order for RAVEL to
work. The only requirement is that the images are registered to the same
template space.
2.1. Prelude
To preprocess the images, we use the packages fslr and ANTsR. The
package fslr is available on CRAN, and requires FSL to be installed on
your machine; see the FSL
website for installation. For
ANTsR, we recommend to install the latest stable version available at
the ANTsR GitHub page. The
version used for this vignette was ANTsR_0.3.2.tgz. For the template
space, we use the JHU-MNI-ss atlas (see Section 1.2) included in the
EveTemplate package, available on GitHub at
https://github.com/Jfortin1/EveTemplate. For data examples, we use 4
T1-w scans from the package RAVELData available on GitHub at
https://github.com/Jfortin1/RAVELData. Once the packages are properly
installed, we are ready to start our preprocessing of T1-w images. We
first load the packages into R:
library(fslr)
library(ANTsR)
library(RAVELData)
library(EveTemplate)
have.fsl() # Should be TRUE if fsl is correctly installed
and let’s specify the path for the different files that we will need:
# JHU-MNI-ss template:
library(EveTemplate)
template_path <- getEvePath("T1")
# JHU-MNI-ss template brain mask:
template_brain_mask_path <- getEvePath("Brain_Mask")
# Example of T1-w MPRAGE image
scan_path <- system.file(package="RAVELData", "extdata/scan1.nii.gz")
2.2. JHU-MNI-ss template (_EVE_ atlas)
2.3. Registration to template
Tp perform a non-linear registration to the JHU-MNI-ss template, one can
use the diffeomorphism algorithm via the ANTsR package. Note that we
perform the registration with the skulls on. Here is an example where we
register the scan1 from the RAVELData package to the JHU-MNI-ss
template:
library(ANTsRCore)
library(ANTsR)
template <- antsImageRead(template_path, 3)
scan <- antsImageRead(scan_path,3)
outprefix <- gsub(".nii.gz","",scan_path) # Prefix for the output files
output <- antsRegistration(fixed = template, moving = scan, typeofTransform = "SyN", outprefix = outprefix)
scan_reg <- antsImageClone(output$warpedmovout) # Registered brain
The object scan_reg contains the scan registed to the template. Note
that the object is in the ANTsR format. Since I prefer to work with
the oro.nifti package, which is compatible with flsr, I convert the
object to a nifti object using the function ants2oro as follows:
# devtools::install_github("muschellij2/extrantsr")
# or
# source("https://neuroconductor.org/sites/default/files/neurocLite.R")
# neuro_install("extrantsr")
library(extrantsr)
scan_reg <- extrantsr::ants2oro(scan_reg)
We can save the registered brain in the NIfTi format using the
writeNIfTI command:
writeNIfTI(scan_reg, "scan_reg")
Since scan_reg is converted to a nifti object, we can use the
function ortho2 from the fslr package to visualize the scan:
ortho2(scan_reg, crosshairs=FALSE, mfrow=c(1,3), add.orient=FALSE)
2.4. Intensity inhomogeneity correction
We perform intensity inhomogeneity correction on the registered scan
using the N4 Correction from the ANTsR package:
scan_reg <- extrantsr::oro2ants(scan_reg) # Convert to ANTsR object
scan_reg_n4 <- n4BiasFieldCorrection(scan_reg)
scan_reg_n4 <- extrantsr::ants2oro(scan_reg_n4) # Conversion to nifti object for further processing
2.5. Skull stripping
template_brain_mask <- readNIfTI(template_brain_mask_path, reorient=FALSE)
scan_reg_n4_brain <- niftiarr(scan_reg_n4, scan_reg_n4*template_brain_mask)
ortho2(scan_reg_n4_brain, crosshairs=FALSE, mfrow=c(1,3), add.orient=FALSE)
2.6. Tissue Segmentation
There are different tissue segmentation algorithms available in R. My
favorite is the FSL FAST segmentation via the
fslr
package. Let’s produce the tissue segmentation for the
scan_reg_n4_brain scan
above:
ortho2(scan_reg_n4_brain, crosshairs=FALSE, mfrow=c(1,3), add.orient=FALSE, ylim=c(0,400))
The last line of code produces via the ortho2 function from the fslr
package the following visualization of the template:
We perform a 3-class tissue segmentation on the T1-w image with the FAST segmentation algorithm:
scan_reg_n4_brain_seg <- fast(scan_reg_n4_brain, verbose=FALSE, opts="-t 1 -n 3")
ortho2(scan_reg_n4_brain_seg, crosshairs=FALSE, mfrow=c(1,3), add.orient=FALSE)
The object scan_reg_n4_brain_seg is an image that contains the
segmentation labels 0,1,2 and 3 referring to Background, CSF, GM and
WM voxels respectively.
2.7. Creation of a tissue mask
Suppose we want to create a mask for CSF.
scan_reg_n4_brain_csf_mask <- scan_reg_n4_brain_seg
scan_reg_n4_brain_csf_mask[scan_reg_n4_brain_csf_mask!=1] <- 0
ortho2(scan_reg_n4_brain_csf_mask, crosshairs=FALSE, mfrow=c(1,3), add.orient=FALSE)
We use the fact that the file scan_reg_n4_brain_seg is equal to 1 for
CSF, 2 for GM and 3 for WM. For instance, a WM mask could be created as
follows:
scan_reg_n4_brain_wm_mask <- scan_reg_n4_brain_seg
scan_reg_n4_brain_wm_mask[scan_reg_n4_brain_wm_mask!=3] <- 0
ortho2(scan_reg_n4_brain_wm_mask, crosshairs=FALSE, mfrow=c(1,3), add.orient=FALSE)
3. Intensity normalization and RAVEL correction
Since MRI intensities are acquired in arbitrary units, image intensities
are not comparable across scans, between subjects and across sites.
Intensity normalization (or intensity standardization) is paramount
before performing between-subject intensity comparisons. The RAVEL
package includes the popular histogram matching normalization
(normalizeHM) as well as the White Stripe normalization
(normalizeWS); see the table below for the reference papers. Once the
images intensities are normalized, the RAVEL correction tool can be
applied using the function normalizeRAVEL to remove additional
unwanted variation using a control region. Because we have found that
the combination White Stripe + RAVEL was best at removing unwanted
variation, the function normalizeRAVEL performs White Stripe
normalization by default prior to the RAVEL correction.
Note: registration is also called spatial normalization which is unrelated to intensity normalization.
Available methods
| Function | Method | Modalities supported at the moment | Paper Link |
| ---------------- | ------------------ | ---------------------------------- | -------------------------------------------------------------------------- |
| normalizeRaw | No normalization | T1, T2, FLAIR, PD | |
| normalizeRAVEL | RAVEL | T1, T2, FLAIR | Link |
| normalizeWS | White Stripe | T1, T2, FLAIR | Link |
| normalizeHM | Histogram Matching | T1, T2, FLAIR, PD | Link |
| normalizeZScore| Z-score transformation | T1, T2, FLAIR, PD | |
Briefly, each function takes as input a list of NIfTI file paths specifying the images to be normalized, and return a matrix of normalized intensities where rows are voxels and columns are scans. We note that the input files must be the files associated with preprocessed images registered to a common template. The different functions are described below.
3.1 No normalization
The function normalizeRaw takes as input the preprocessed and
registered images, and create a matrix of voxel intensities without
intensity normalization. For conventional MRI images, we recommend to
apply an intensity normalization to the images (see normalizeWS or
normalizeRAVEL). The main purpose of the function normalizeRaw is
for exploration data analysis (EDA), methods development and methods
comparison.
| Argument | Description | Default |
| -------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | ------- |
| input.files | vector or list of the paths for the input NIfTI image files to be normalized | |
| output.files | Optionnal vector or list of the paths for the output images. By default, will be the input.files with "_RAW" appended at the end. | NULL |
| brain.mask | NIfTI image path for the binary brain mask. Must have value 1 for the brain and 0 otherwise | |
| returnMatrix | Should the matrix of normalized images be returned? Rows correspond to voxels specified by brain.mask, and columns correspond to scans. | TRUE |
| writeToDisk | Should the normalized images be saved to the disk as NIfTI files? | FALSE |
| verbose | Should the function be verbose? | TRUE |
3.2 White Stripe normalization
The function normalizeWS takes as input the preprocessed and
registered images, applies the White Stripe normalization algorithm to
each image separately via the WhiteStripe R package, and creates a
matrix of normalized voxel intensities. Note that the White Stripe
normalization is also included as a first step in the RAVEL algorithm
implemented in the normalizeRAVEL
function.
| Argument | Description | Default |
| ------------------ | ----------------------------------------------------------------------------------------------------------------------------------------- | ------- |
| input.files | vector or list of the paths for the input NIfTI image files to be normalized | |
| output.files | Optionnal vector or list of the paths for the output images. By default, will be the input.files with "_WS" appended at the end. | NULL |
| brain.mask | NIfTI image path for the binary brain mask. Must have value 1 for the brain and 0 otherwise | |
| WhiteStripe_Type | What is the type of images to be normalized? Must be one of “T1”, “T2” and “FLAIR”. | T1 |
| returnMatrix | Should the matrix of normalized images be returned? Rows correspond to voxels specified by brain.mask, and columns correspond to scans. | TRUE |
| writeToDisk | Should the normalized images be saved to the disk as NIfTI files? | FALSE |
| verbose | Should the function be verbose? | TRUE |
3.3 Histogram matching normalization
The function normalizeHM takes as input the preprocessed and
registered images, applies the Histogram matching normalization algorithm to
each image separately, and creates a
matrix of normalized voxel intensities.
| Argument | Description | Default |
| ------------------ | ----------------------------------------------------------------------------------------------------------------------------------------- | ------- |
| input.files | vector or list of the paths for the input NIfTI image files to be normalized | |
| output.files | Optionnal vector or list of the paths for the output images. By default, will be the input.files with "_WS" appended at the end. | NULL |
| brain.mask | NIfTI image path for the binary brain mask. Must have value 1 for the brain and 0 otherwise | |
| type | What is the type of images to be normalized? Must be one of “T1”, “T2”, "PD", and “FLAIR”. | T1 |
| returnMatrix | Should the matrix of normalized images be returned? Rows correspond to voxels specified by brain.mask, and columns correspond to scans. | TRUE |
| writeToDisk | Should the normalized images be saved to the disk as NIfTI files? | FALSE |
| verbose | Should the function be verbose? | TRUE |
3.4 Z-score transformation (whole-brain)
The function normalizeZScore takes as input the preprocessed and
registered images, applies a whole-brain z-score transformation to
each image separately, and creates a
matrix of normalized voxel intensities.
| Argument | Description | Default |
| ------------------ | ----------------------------------------------------------------------------------------------------------------------------------------- | ------- |
| input.files | vector or list of the paths for the input NIfTI image files to be normalized | |
| output.files | Optionnal vector or list of the paths for the output images. By default, will be the input.files with "_WS" appended at the end. | NULL |
| brain.mask | NIfTI image path for the binary brain mask. Must have value 1 for the brain and 0 otherwise | |
| type | What is the type of images to be normalized? Must be one of “T1”, “T2”, "PD", and “FLAIR”. | T1 |
| returnMatrix | Should the matrix of normalized images be returned? Rows correspond to voxels specified by brain.mask, and columns correspond to scans. | TRUE |
| writeToDisk | Should the normalized images be saved to the disk as NIfTI files? | FALSE |
| verbose | Should the function be verbose? | TRUE |
3.5 RAVEL normalization
The function normalizeRAVEL takes as input the preprocessed and
registered images, and a control region mask, and applies the RAVEL
correction method to create a matrix of normalized voxel intensities.
The White Stripe normalization is included by default as a first step in
the RAVEL algorithm. The next section explains how to create a control
region
mask.
| Argument | Description | Default |
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------- |
| input.files | vector or list of the paths for the input NIfTI image files to be normalized | |
| output.files | Optionnal vector or list of the paths for the output images. By default, will be the input.files with "_RAVEL" appended at the end. | NULL |
| brain.mask | NIfTI image path for the binary brain mask. Must have value 1 for the brain and 0 otherwise | |
| control.mask | NIfTI image path for the binary control region mask. Must have value 1 for the control region and 0 otherwise. See the helper function mask_intersect for the creation of a control.mask. | |
| mod | Model matrix for outcome of interest and other covariates | NULL |
| WhiteStripe | Should White Stripe normalization be performed before RAVEL? | TRUE |
| WhiteStripe_Type | If WhiteStripe is TRUE, what is the type of images to be normalized? Must be one of “T1”, “T2” and “FLAIR”. | T1 |
| k | Integer specifying the number of principal components to be included in the RAVEL correction. | 1 |
| returnMatrix | Should the matrix of normalized images be returned? Rows correspond to voxels specified by brain.mask, and columns correspond to scans. | TRUE |
| writeToDisk | Should the normalized images be saved to the disk as NIfTI files? | FALSE |
| verbose | Should the function be verbose? | TRUE |
3.6 Creation of a control region for RAVEL
RAVEL uses a control region of the brain to infer unwanted variation
across subjects. The control region is made of voxels known to be
not associated with the phenotype of interest. For instance, it is
known that CSF intensities on T1-w images are not associated with the
progression of AD. The control region must be specified in the argument
control.mask of the function normalizeRAVEL as a path to a NIfTI
file storing a binary mask. In the case of a CSF control region, one way
to create such a binary mask is to create a CSF binary mask for each
scan first, and then to take the intersection of all those binary masks. This
can be done with the function maskIntersect in RAVEL. The function takes as
input a list of binary masks (either nifti objects or a list of NIfTI
file paths), and will output the intersection of all the binary masks.
By default, the function will save the intersection mask to the disk as
a NIfTI file, as specified by
output.file:
Example:
dir <- file.path(find.package("RAVELData"), "extdata")
masks <- list.files(dir, full.names=TRUE, pattern="*mask*.nii*")
intersect_mask <- maskIntersect(masks, output.file=tempfile())
When the number of subjects is large, the intersection mask may be
empty, as a consequence of anatomical variation between subjects. As a
solution, the function maskIntersect has the option to create an
intersection mask that is less stringent by requiring the control region
to be present in only a given percentage of the subjects, using the
option prob. By default, prob is equal to 1, meaning 100% of the
subjects has the final voxels labelled as CSF. For instance, to require
that the final control region is shared for at least 90% of the
subjects, one would
type
intersect_mask <- maskIntersect(masks, output.file=tempfile(), prob=0.9)
For studies with a small number of subjects, the opposite problem may arise: too many voxels labelled as CSF, close to the skull, might be retained in the final intersection mask. Mask erosion, for instance using fslr, may be performed to remove such voxels and refine the control mask.
3.7 Controlling for biological covariates in RAVEL
In removing the unwanted variation estimated using control voxels, it is possible to preserve biological variation by specifying biological covariates in the normalizeRAVEL function, similar to ComBat harmonization.
For instance, suppose we want to normalize intensities across participants using CSF, and also want to make sure that we don't remove variation in intensities associated with age. We first need to build a model matrix for the biological covariates (here age):
age <- c(70,62,43,76) #Simulated age
gender <- c("M", "M", "F", "F")
mod <- model.matrix(~age+gender)
Note that while the model matrix has an intercept column, this will be automatically handled internally by the normalizeRAVEL function. To run RAVEL while adjusting for age and gender, we include mod as an argument as follows:
Y.ravel.mod <- normalizeRAVEL(input.files=input.files,
control.mask=control.mask,
brain.mask=brain.mask,
k=1,
mod=mod,
returnMatrix=TRUE,
WhiteStripe=FALSE
)
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