BrainAGE

Estimate BrainAge from a T1-weighted MR image using the DeepBrainNet architecture and weights described here:

brainAge(
  image,
  doPreprocessing = TRUE,
  numberOfSimulations = 0,
  sdAffine = 0.01,
  antsxnetCacheDirectory = NULL,
  verbose = TRUE
)

Arguments

image	input 3-D T1-weighted brain image.
doPreprocessing	boolean dictating whether prescribed preprocessing is performed (brain extraction, bias correction, normalization to template).
numberOfSimulations	number of random affine perturbations to transform the input.
sdAffine	define the standard deviation of the affine transformation parameter for the simulations.
antsxnetCacheDirectory	destination directory for storing the downloaded template and model weights. Since these can be resused, if is.null(antsxnetCacheDirectory), these data will be downloaded to the inst/extdata/ subfolder of the ANTsRNet package.
verbose	print progress.

Value

predicted age and binned confidence values

Details

https://github.com/vishnubashyam/DeepBrainNet

and described in the following article:

https://pubmed.ncbi.nlm.nih.gov/32591831/

Preprocessing on the training data consisted of:

• n4 bias correction,

• brain extraction, and

• affine registration to MNI. The input T1 should undergo the same steps. If the input T1 is the raw T1, these steps can be performed by the internal preprocessing, i.e. set doPreprocessing = TRUE

Author

Tustison NJ

Examples

if (FALSE) {
library( ANTsRNet )
library( keras )

image <- antsImageRead( "t1w_image.nii.gz" )
estimatedBrainAge <- brainAge( image )
}