neuronorm (version 1.0.2)

preprocess_modalities: Preprocess group of MRI scan for one patient

Description

This function preprocesses raw T1-weighted, T2-weighted and FLAIR MRI scans and generates a segmentation MRI scan using the FAST algorithm. The preprocesising steps comprises imhomogeneity correction 'N4', coregistration of other sequences to the T1-weighted scan, non-linear registration to the MNI152 template with an isotropic voxel size of 1mm, using the 'SyN' transformation, skull stripping, brain segmentation and intensity normalization using the RAVEL or White Stripe algorithms.

Usage

preprocess_modalities(
  mri.patient,
  folder.patient,
  modalities,
  atlas,
  mask,
  inhomogeneity = "N4",
  transformation = "SyN"
)

Value

paths of preprocessed MRI scans.

Arguments

mri.patient

path of the MRI scans.

folder.patient

folder containing the MRI scans. This folder usually refers to the patient.

modalities

vector of strings containing the modalities to be preprocessed. It must always contains the T1-weighted sequence scan.

atlas

atlas template in NifTI format to spatially register the MRI scans. By default the MNI152 atlas template is used.

mask

brain mask in NifTI format of the atlas template to performed the skull stripping.

inhomogeneity

inhomogeneity correction algorithm to be applied. The correction by default is the 'N4' bias correction.

transformation

non-linear transformation for registering the T1-w MRI scan to the reference template. 'SyN' transformation is used by default.

Author

David Payares

References

Nicholas J. Tustison, Brian B. Avants, Philip A. Cook, Yuanjie Zheng, Alexander Egan, Paul A. Yushkevich, and James C. Gee. N4ITK: Improved N3 Bias Correction. IEEE Trans Med Imaging, 29:1310–1320, 2010.

B. B. Avants, C. L. Epstein, M Grossman, J. C. Gee Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12:1310–1320, 2008.

Evans, A.C., Fox, P.T., Lancaster, J., Zilles, K., Woods, R., Paus, T., Simpson, G., Pike, B., Holmes, C., Collins, D.L., Thompson, P., MacDonald, D., Iacoboni, et al. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos. Trans. R. Soc. London B Biol, 356:1293-1322, 2001.

Yongyue Zhang, J. Michael Brady, Stephen Smith Hidden Markov random field model for segmentation of brain MR image. Medical Imaging 2000: Image Processing, 2000.

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

if (FALSE) {
# 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 = ';')

# Folder of the patient
patient_folder <- file.path(folder,"patient01")

# Getting the paths of the MRI scan sequences for one patient
# the NeuroNorm built-in function load_mri_patient() can be used for this.
sequences <- load_mri_patient(patient_folder)

# Getting preferred atlas template and template mask
# Using the MNI152 template available in the MNITemplate package
library(MNITemplate)
atlas <- getMNIPath()
atlas_mask <- readMNI("Brain_Mask")

# Preprocessing the patient's sequences
patient_preprocessed_mri <- preprocess_modalities(mri.patient = sequences,
                                                  folder.patient = patient_folder,
                                                  modalities = c('T1','T2','FLAIR'),
                                                  atlas = atlas, mask = atlas_mask,
                                                  inhomogeneity = 'N4',
                                                  transformation = 'SyN')


}