image_normalization_ravel {neuronorm}

Wrapper function for RAVEL normalization of T1-weighted images

Description

Ravel intensity normalization using control voxels and clinical covariates.

Usage

image_normalization_ravel(
  masked.paths,
  csf.paths,
  ravel.paths,
  demographics,
  brain.mask,
  patients.folder,
  modality = "T1"
)

Arguments

masked.paths

list or vector of paths of the preprocessed input NIfTI images to be normalized.

csf.paths

NIfTI image paths for the binary control region masks.

ravel.paths

list or vector of paths of the output NIfTI images.

demographics

table of covariates associated to the MRI scans. Number of rows should be equal to the number of images.

brain.mask

NIfTI image path for the binary brain mask. Must have value 1 for the brain tissue and 0 otherwise.

patients.folder

folder to save the output control mask.

modality

string describing the modality to perform the normalization. It should be one of T1, T2 or FLAIR.

Value

RAVEL-corrected images are saved in disk.

Author(s)

David Payares

References

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.

Examples

## Not run: 
# Get general folder
folder <- system.file("extdata", package = "neurodata")
# Get covariates
covariates <- system.file("covariates.txt", package = "neurodata")
# Read covariates information
clinical_info <- read.csv(file = covariates, sep = ';')

# Defining the RAVEL output files for the patients
# with a T2-weighted sequence (patient 1,2 and 4)
patients <- c(1,2,4)
output_files <- lapply(patients, function(x) {
           file.path(folder, paste0("patient0",x),"T2_ravel.nii.gz")})

# Getting the files of the preprocessed images (without intensity normalization)
# and the CSF masks computed by the preprocessing.
csf_paths <- lapply(paths_preprocess_patients[patients], function(x){x$csf_mask})
masked_paths <- lapply(paths_preprocess_patients[patients], function(x){x$stripped[2]})

Subseting covariares info
cov_pat <- clinical_info[clinical_info$patient %in% patients,]

Normalizing T2 sequences with RAVEL
image_normalization_ravel(masked.paths = masked_paths, csf.paths = csf_paths,
                         ravel.paths = output_files, demographics = cov_pat,
                         brain.mask = atlas_mask, patients.folder = folder,
                         modality = "T2")


## End(Not run)