aslPerfusion.RdThis function will estimate perfusion from an ASL time series using a (robust) regression approach. It will do motion correction, compcorr, factor out nuisance variables and use regression to estimate the perfusion itself. It will mask the image too based on a simple procedure ( should fix this in the future by allowing the user to optionally pass a mask in from the outside ). WARNING: the function will estimate the m0 image from the mean of the control tags assuming that the data is acquired T C T C as is most of JJ's data. Quantitative CBF can be obtained by mutiplying the output of this function by a scalar.
aslPerfusion( asl, maskThresh = 0.75, moreaccurate = 1, dorobust = 0.92, m0 = NA, skip = 20, mask = NULL, checkmeansignal = 100, moco_results = NULL, regweights = NULL, useDenoiser = NULL, useBayesian = 0, verbose = FALSE, ncompcor = 0, N3 = FALSE )
| asl | input asl image |
|---|---|
| maskThresh | for estimating a brain mask |
| moreaccurate | zero, one or two with the last being the most accurate |
| dorobust | robustness parameter, lower value keeps more data |
| m0 | known M0 if any |
| skip | stride to speed up robust regression weight estimates |
| mask | known brain mask |
| checkmeansignal | throw out volumes with mean lower than this thresh |
| moco_results | passes prior motion results so moco does not get repeated |
| regweights | known temporal weights on regression solution, if any |
| useDenoiser | use the aslDenoiser if this value is a range gt than zero |
| useBayesian | strength of bayesian prior |
| verbose | bool |
| ncompcor | number of compcor parameters |
| N3 | bool correct target image before motion corr |
output a list of relevant objects
Avants BB
# image available at http://files.figshare.com/1701182/PEDS012_20131101.zip # fn<-'PEDS012_20131101_pcasl_1.nii.gz' # asl<-antsImageRead(fn,4) set.seed(1) nvox <- 5*5*5*10 dims <- c(5,5,5,10) asl <- makeImage( dims , rnorm( nvox )+500 ) %>% iMath("PadImage" , 2 ) aslmean <- getAverageOfTimeSeries( asl ) aslmask <- getMask( aslmean , 0.001 , Inf ) aslmat<-timeseries2matrix( asl, aslmask ) for ( i in 1:10 ) aslmat[,i*2]<-aslmat[,i*2]*2 asl<-matrix2timeseries( asl, aslmask, aslmat ) # NOT WORKING if (FALSE) { pcasl.processing <- aslPerfusion( asl, moreaccurate=1, dorobust=0 ) testthat::expect_equal(mean(pcasl.processing$m1), 62.2115522470984) pcasl.processing <- aslPerfusion( asl, moreaccurate=1, ncompcor=2 ) # allow some rejection pcasl.processing <- aslPerfusion( asl, moreaccurate=1, dorobust=0.925 ) }