quantifyCBF

Computes CBF from ASL - pasl or pcasl

quantifyCBF(perfusion, mask, parameters, M0val = NA, outlierValue = 0.02)

Arguments

perfusion

input asl matrix

mask

3D image mask (antsImage)

parameters

list with entries for sequence and m0 (at minimimum)

M0val

baseline M0 value (optional)

outlierValue

trim outliers by this fractional value (optional)

Value

a list is output with 3 types of cbf images

Author

Avants BB, Kandel B, Duda JT

Examples

if (FALSE) { # \dontrun{
if (!exists("fn")) {
  fn <- getANTsRData("pcasl")
}
# PEDS029_20101110_pcasl_1.nii.gz # high motion subject
asl <- antsImageRead(fn)
# image available at http://files.figshare.com/1701182/PEDS012_20131101.zip
pcasl.bayesian <- aslPerfusion(asl,
  dorobust = 0., useDenoiser = 4, skip = 11, useBayesian = 1000,
  moreaccurate = 0, verbose = T, maskThresh = 0.5
) # throw away lots of data
# user might compare to useDenoiser=FALSE
pcasl.parameters <- list(sequence = "pcasl", m0 = pcasl.bayesian$m0)
cbf <- quantifyCBF(
  pcasl.bayesian$perfusion, pcasl.bayesian$mask,
  pcasl.parameters
)
meancbf <- cbf$kmeancbf
print(mean(meancbf[pcasl.bayesian$mask == 1]))
antsImageWrite(meancbf, "temp.nii.gz")
pcasl.processing <- aslPerfusion(asl,
  moreaccurate = 0,
  dorobust = 0.95, useDenoiser = NULL, skip = 5, useBayesian = 0
)
# user might compare to useDenoiser=FALSE
pcasl.parameters <- list(sequence = "pcasl", m0 = pcasl.processing$m0)
cbf <- quantifyCBF(pcasl.processing$perfusion, pcasl.processing$mask, pcasl.parameters)
meancbf <- cbf$kmeancbf
print(mean(meancbf[pcasl.processing$mask == 1]))
antsImageWrite(meancbf, "temp2.nii.gz")
plot(meancbf, slices = "1x50x1")
} # }