View source: R/preprocessing.R
image_normalization_ravel | R Documentation |
Ravel intensity normalization using control voxels and clinical covariates.
image_normalization_ravel( masked.paths, csf.paths, ravel.paths, demographics, brain.mask, patients.folder, modality = "T1" )
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. |
RAVEL-corrected images are saved in disk.
David Payares
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.
## 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)
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