preprocessBrainImage: Basic preprocessing pipeline for T1-weighted brain MRI

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

Basic preprocessing pipeline for T1-weighted brain MRI

Description

Various preprocessing steps that have been previously described in various papers including the cortical thickness pipeline:

Usage

preprocessBrainImage(
  image,
  truncateIntensity = c(0.01, 0.99),
  brainExtractionModality = NULL,
  templateTransformType = NULL,
  template = "biobank",
  doBiasCorrection = TRUE,
  returnBiasField = FALSE,
  doDenoising = TRUE,
  intensityMatchingType = NULL,
  referenceImage = NULL,
  intensityNormalizationType = NULL,
  verbose = TRUE
)

Arguments

image	input t1-weighted brain MRI
truncateIntensity	2-element vector giving the low and high quantiles for intensity truncation.
brainExtractionModality	string or NULL. Perform brain extraction using antsxnet tools. One of "t1", "t1v0", "t1nobrainer", "t1combined", "flair", "t2", "bold", "fa", "t1infant", "t2infant", or NULL.
templateTransformType	see Details in help for antsRegistration. Typically "Rigid" or "Affine".
template	an ANTs image (not skull-stripped). Other premade templates are "biobank" and "croppedMni152".
doBiasCorrection	boolean for performing N4 bias field correction.
returnBiasField	if TRUE, return bias field as an additional output without bias correcting the preprocessed image.
doDenoising	boolean for performing non-local means denoising.
intensityMatchingType	Either "regression" or "histogram". Only is performed if !is.null(referenceImage).
referenceImage	reference image for intensity matching.
intensityNormalizationType	Either rescale the intensities to 0,1 (i.e., "01") or zero-mean, unit variance (i.e., "0mean"). If NULL normalization is not performed.
verbose	print progress to the screen.

Details

\url{https://www.ncbi.nlm.nih.gov/pubmed/24879923}

Value

preprocessed image and, optionally, the brain mask, bias field, and template transforms.

Author(s)

Tustison NJ, Avants BB

Examples

library( ANTsR )
library( ANTsRNet )

image <- antsImageRead( getANTsRData( "r16" ) )
preprocessedImage <- preprocessBrainImage( image,
  truncateIntensity = c( 0.01, 0.99 ),
  doBiasCorrection = TRUE, doDenoising = TRUE,
  intensityNormalizationType = "01", verbose = FALSE )

ANTsX/ANTsRNet documentation built on Nov. 21, 2024, 4:07 a.m.