preprocess_patients: Preprocess MRI scans for multiple patients

In neuronorm: Preprocessing of Structural MRI for Multiple Neurodegenerative Diseases

Preprocess MRI scans for multiple patients

Description

This function preprocesses raw T1-weighted, T2-weighted and/or FLAIR MRI scans and generates a brain segmentation MRI scans using the FAST algorithm. The preprocessing steps comprise imhomogeneity correction 'N4', linear coregistration of T2-weighted and/or FLAIR to the T1-weighted, registration of all available modalities to the MNI152 template with an isotropic voxel size of 1mm^3 using the 'SyN' transformation, skull stripping, and RAVEL intensity normalization.

Usage

preprocess_patients(patients.folder, clinical.covariates)

Arguments

patients.folder	general folder containing sub-folders per patient with raw MRI images.
clinical.covariates	data.frame of covariates associated to the MRI scans. Number of rows should be equal to the number of images.

Value

paths of preprocessed MRI scans. MRI preprocessed images are stored in the patient's folder.

Author(s)

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

## 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 = ';')
# Preprocess MRI scans: 'N4' inhomogeneity correction,
# 'SyN' non-linear transformation to MNI152 atlas template

# Brain extraction, Spatial informed MRI scan , a.k.a., brain segmentation
# and RAVEL intensity normalization only for T1-w images.
paths_preprocess_patients <- preprocess_patients(folder, clinical_info)
# Outputs paths of the preprocessed MRI scans per patient
# and applied preprocessing.
paths_preprocess_patients$patient02

## End(Not run)

neuronorm documentation built on Sept. 24, 2022, 1:08 a.m.