ANTsR (version 0.3.1)

bayesianCBF: Uses probabilistic segmentation to constrain pcasl-based cbf computation.

Description

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.

Usage

bayesianCBF(pcasl, segmentation, tissuelist, myPriorStrength = 30,
  useDataDrivenMask = 3, denoisingComponents = 1:8,
  robustnessvalue = 0.95, localweights = F, 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

Examples

# NOT RUN {
set.seed(123)
# see fMRIANTs github repository for data and I/O suggestions
# }