ANTsR (version 0.3.1)

aslCensoring: Censor bad volumes from ASL data.

Description

Censor bad volumes from ASL data.

Usage

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

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.

Value

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

Details

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

1. 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.

2. 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).

3. scor SCOR method of Dolui et al. No parameters.

References

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

Examples

# NOT RUN {
nvox <- 5 * 5 * 5 * 10
dims <- c(5, 5, 5, 10)
voxvals <- array(rnorm(nvox) + 500, dim=dims)
voxvals[, , , 5] <- voxvals[, , , 5] + 600
asl <- makeImage(dims, voxvals)
censored <- aslCensoring(asl)
# }