Perfusion Regression

Estimate CBF using standard regression and optionally robust regression.

perfusionregression(
  mask_img,
  mat,
  xideal,
  nuis = NA,
  dorobust = 0,
  skip = 20,
  selectionValsForRegweights = NULL,
  useBayesian = 0
)

Arguments

mask_img

Mask image selects the voxels where CBF will be estimated. Voxels corresponding to logical FALSE are not computed.

mat

Matrix with a column for every time-series voxel. Number of rows equals the number of time units in the series.

xideal

1D time-series signal to be used a ideal or model for regression.

nuis

Nuisance parameters obtained from '.get_perfusion_predictors'.

dorobust

Real value in interval from 0 to 1. If greater than 0, then robust regression will be performed. A typical value would be 0.95 i.e. use voxels with 95 percent confidence.

skip

skip / stride over this number of voxels to increase speed

selectionValsForRegweights

scalar function to guide parameter est.

useBayesian

if greater than zero, use a bayesian prior w/this weight

Value

Success – An object of type 'antsImage' containing the CBF estimate for voxels corresponding to the mask input

Author

Shrinidhi KL Avants BB

Examples

if (FALSE) { # \dontrun{
#
# cbf <- perfusionregression( mask_img, mat, xideal , nuis )
} # }