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

Examples

  if (FALSE) {
  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" )
  }