aslDenoiseR.RdDenoises regression based reconstruction of CBF from arterial spin labeling
aslDenoiseR(
boldmatrix,
targety,
covariates = NA,
selectionthresh = 0.1,
maxnoisepreds = 2:12,
polydegree = "loess",
crossvalidationgroups = 4,
scalemat = F,
noisepoolfun = max,
usecompcor = F,
verbose = F
)input bold matrix
target to predict
motion or other parameters / nuisance variables
e.g. 0.1 take 10 percent worst variables for noise estimation
integer search range e.g 1:10
eg 4 for polynomial nuisance variables or 'loess'
prior defined or integer valued
boolean
function to help select noise pool e.g. max
boolean
boolean
matrix is output
# 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,
covariates = dv, selectionthresh = 0.1,
maxnoisepreds = c(1:2), polydegree = 2, crossvalidationgroups = 2
)
testthat::expect_equal(dnz$R2atBestN, 7, tolerance = 0.5)
if (FALSE) { # \dontrun{
# a classic regression approach to estimating perfusion
# not recommended, but shows the basic idea.
# see ?quantifyCBF for a better approach
perfmodel <- lm(aslmat ~ tc + dnz$noiseu)
perfimg <- antsImageClone(aslmask)
perfimg[aslmask == 1] <- bigLMStats(perfmodel)$beta[1, ]
m0 <- getAverageOfTimeSeries(asl)
ctl <- c(1:(nrow(aslmat) / 2)) * 2
m0[aslmask == 1] <- colMeans(aslmat[ctl, ])
pcasl.parameters <- list(sequence = "pcasl", m0 = m0)
cbf <- quantifyCBF(perfimg, aslmask, pcasl.parameters)
# default mode network example
if (!exists("bold")) {
bold <- antsImageRead(getANTsRData("rsbold"))
meanbold <- getAverageOfTimeSeries(bold)
boldmask <- getMask(meanbold)
# map to mni
mni <- antsImageRead(getANTsRData("mni"))
mniaal <- antsImageRead(getANTsRData("mnia"))
mymap <- antsRegistration(meanbold * boldmask, mni,
typeofTransform = "SyNBold",
verbose = 1
)
aalimg <- antsApplyTransforms(meanbold, mniaal, mymap$fwdtransforms,
interpolator = "NearestNeighbor"
)
data("aal", package = "ANTsR")
timeselect <- 10:dim(bold)[4]
if (!exists("moco")) {
moco <- antsMotionCalculation(bold, boldmask)
}
sbold <- smoothImage(moco$moco_img, 3.0)
antsImageWrite(boldmask, "boldmask.nii.gz")
antsImageWrite(meanbold, "boldmean.nii.gz")
antsImageWrite(aalimg, "boldaal.nii.gz")
boldmask <- boldmask * thresholdImage(aalimg, 1, Inf)
}
postcing <- aal$label_num[grep("Cingulum_Post", aal$label_name)]
postCingMask <- maskImage(boldmask, aalimg,
level = as.numeric(postcing), binarize = T
)
mpostCingMask <- antsImageClone(postCingMask) * 0
mpostCingMask[postCingMask == 0] <- 1
boldmat <- timeseries2matrix(sbold, boldmask * mpostCingMask)
boldmat <- boldmat[timeselect, ]
boldmat <- frequencyFilterfMRI(boldmat, tr = antsGetSpacing(bold)[4], opt = "trig")
dmnvec <- (timeseries2matrix(sbold, postCingMask)[timeselect, ])
dmnvec <- rowMeans(
frequencyFilterfMRI(dmnvec, tr = antsGetSpacing(bold)[4], opt = "trig")
)
dmnmat <- matrix(dmnvec, ncol = 1)
mocpar <- moco$moco_params[timeselect, 3:14]
dnz <- aslDenoiseR(boldmat, dmnvec,
covariates = mocpar, selectionthresh = 0.2,
maxnoisepreds = c(2:10), polydegree = "loess",
crossvalidationgroups = 8
)
boldmat <- timeseries2matrix(sbold, boldmask)
boldmat <- boldmat[timeselect, ]
boldmat <- frequencyFilterfMRI(boldmat, tr = antsGetSpacing(bold)[4], opt = "trig")
mdl <- bigLMStats(lm(boldmat ~ dmnvec + dnz$covariates + dnz$noiseu), 0.001)
betas <- mdl$beta.t[1, ]
betaImg <- makeImage(boldmask, betas)
antsImageWrite(betaImg, "dmnBetas.nii.gz")
# this should give default mode network around beta = 12
} # }