Uses probabilistic segmentation to constrain pcasl-based cbf computation.

Employs a robust regression approach to learn the relationship between a sample image and a list of images that are mapped to the same space as the sample image.

bayesianCBF(
  pcasl,
  segmentation,
  tissuelist,
  myPriorStrength = 30,
  useDataDrivenMask = 3,
  denoisingComponents = 1:8,
  robustnessvalue = 0.95,
  localweights = FALSE,
  priorBetas = NA
)

Arguments

pcasl

img antsImage for cbf

segmentation

image, should cover the brain.

tissuelist

a list containing antsImages eg list(prob1,...,probN)

myPriorStrength

- e.g 30

useDataDrivenMask

- morphology parameters e.g. 3

denoisingComponents

- data-driven denoising parameters

robustnessvalue

- value (e.g. 0.95) that throws away time points

localweights

Use estimate of voxel-wise reliability to inform prior weight?

priorBetas

prior betas for each tissue and predictor

Value

estimated cbf image

Author

Brian Beaumont Avants and Benjamin M. Kandel

Examples

if (FALSE) { # \dontrun{
set.seed(123)
# see fMRIANTs github repository for data and I/O suggestions
} # }