aslCensoring: Censor bad volumes from ASL data.

In stnava/ANTsR: ANTs in R: Quantification Tools for Biomedical Images

Censor bad volumes from ASL data.

Description

Censor bad volumes from ASL data.

Usage

aslCensoring(
  asl,
  mask = NULL,
  nuis = NA,
  method = "outlier",
  reject.pairs = FALSE,
  ...
)

Arguments

asl	input asl image
mask	mask for calculating perfusion
nuis	fixed nuisance parameters
method	one of 'outlier', 'robust', or 'scor'. See Details.
reject.pairs	whether to reject only tag-control pairs of images, as opposed to single images. Rejecting pairs of images is necessary for non-regression-based ASL averaging methods.
...	Additional arguments to pass to censoring method. See Details.

Details

aslCensoring is an interface to ASL timepoint censoring algorithms. Three options are currently provided, with different additional arguments:

outlier

Outlier rejection from Tan et al. This method rejects volumes that are either far from the mean of the time-series or whose standard deviation is far from the standard deviations of the individual volumes. Accepts two additional arguments:

sigma.mean:

how many standard deviations the mean of the volume can be from the mean of all the volumes before being thrown out.

sigma.sd:

how many standard deviations from the mean of standard deviations can the standard deviation of the volume be before being thrown out.

robust

Uses a robust regression approach to estimate volumes with high leverage. Accepts three arguments:

nuis:

Nuisance regressors to use as covariates.

robthresh:

Threshold for weights on leverage estimates. Points with weights under this value will be thrown out; defaults to 0.95.

skip:

Proportion of points to skip when estimating leverage. Defaults to 20 (1/20 of the image is used).

scor

SCOR method of Dolui et al. No parameters.

Value

vector of the same length as number of timepoints in asl, with 1 indicating the corresponding timepoint is included and 0 indicating exclusion.

Author(s)

Kandel BM

References

Tan H. et al., “A Fast, Effective Filtering Method for Improving Clinical Pulsed Arterial Spin Labeling MRI,” JMRI 2009.

Examples

set.seed(1)
nvox <- 5 * 5 * 5 * 30
dims <- c(5, 5, 5, 30)
voxvals <- array(rnorm(nvox) + 500, dim = dims)
voxvals[, , , 5] <- voxvals[, , , 5] + 600
asl <- makeImage(dims, voxvals)
censored <- aslCensoring(asl)

stnava/ANTsR documentation built on April 13, 2025, 4:10 a.m.