ANTsR (version 0.3.1)

aslDenoiseR: WIP: data-driven denoising for ASL MRI

Description

Denoises regression based reconstruction of CBF from arterial spin labeling

Usage

aslDenoiseR(boldmatrix, targety, motionparams = NA, selectionthresh = 0.1,
  maxnoisepreds = 1:12, debug = FALSE, polydegree = 4,
  crossvalidationgroups = 4, scalemat = F, noisepoolfun = max,
  usecompcor = F)

Arguments

boldmatrix

input bold matrix

targety

target to predict

motionparams

motion parameters / nuisance variables

selectionthresh

e.g. 0.1 take 10 percent worst variables for noise estimation

maxnoisepreds

integer search range e.g 1:10

debug

boolean

polydegree

eg 4 for polynomial nuisance variables

crossvalidationgroups

prior defined or integer valued

scalemat

boolean

noisepoolfun

function to help select noise pool e.g. max

usecompcor

boolean

Value

matrix is output

Examples

# NOT RUN {
# asl<-antsImageRead( getANTsRData("pcasl") )
set.seed(1)
nvox <- 10*10*10*20
dims <- c(10,10,10,20)
asl <- makeImage( dims , rnorm( nvox )+500 )
aslmean <- getAverageOfTimeSeries( asl )
aslmask <- getMask( aslmean  )
aslmat<-timeseries2matrix( asl, aslmask )
for ( i in 1:10 ) aslmat[,i*2]<-aslmat[,i*2]*2
asl<-matrix2timeseries( asl, aslmask, aslmat )
tc<-as.factor(rep(c("C","T"),nrow(aslmat)/2))
dv<-computeDVARS(aslmat)
dnz<-aslDenoiseR( aslmat, tc, motionparams=dv, selectionthresh=0.1,
  maxnoisepreds=c(1:2), debug=TRUE, polydegree=2, crossvalidationgroups=2 )
# }
# NOT RUN {
# a classic regression approach to estimating perfusion
# not recommended, but shows the basic idea.
# see ?quantifyCBF for a better approach
perfmodel<-lm( aslmat ~ tc + dnz$noiseu  )
perfimg<-antsImageClone(aslmask)
perfimg[ aslmask == 1 ]<-bigLMStats( perfmodel )$beta[1,]
m0<-getAverageOfTimeSeries(asl)
ctl<-c(1:(nrow(aslmat)/2))*2
m0[ aslmask==1 ]<-colMeans(aslmat[ctl,])
pcasl.parameters<-list( sequence="pcasl", m0=m0 )
cbf <- quantifyCBF( perfimg, aslmask, pcasl.parameters )
# }