qMRI (version 1.2.7)

readIRData: Prepare IRMRI dataset

Description

The function reads IRMRI images given as NIfTI files in t1Files, inversion times and segmentation image(s) aund prepares an object class "IRdata"

Usage

readIRData(t1Files, InvTimes, segmFile, sigma = NULL, L = 1,
           segmCodes = c("GM", "WM", "CSF"))

Value

A list of class "IRdata" with components

IRdata

4D array containing the IRMRI data, first dimension refers to inversion times

InvTimes

vector of inversion times

segm

segmentation codes, 1 for CSF, 2 for GM, 3 for WM, 0 for out of brain

sigma

noise standard deviation, if not specified estimated fron CSF areas in image with largest inversion time

L

effective number of coils

Arguments

t1Files

Names of NIfTI files containing the recorded images.

InvTimes

Corresponding inversion times

segmFile

Either a NIfTI file containing a segmentation into GM, WM and CSF or three files containing probability maps for GM, WM and CSF

sigma

Noise standard deviation

L

Effective number of coils, L=1 assumes a Rician signal distribution

segmCodes

sequence of tissue code in segmFile

Author

Karsten Tabelow
J\"org Polzehl

See Also

estimateIRfluid, estimateIRsolid, estimateIR,smoothIRSolid

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (t1Files, InvTimes, segmFile, sigma = NULL, L = 1, segmCodes = c("GM", 
    "WM", "CSF")) 
{
    if (is.null(t1Files)) 
        stop("vector of T1 files required")
    nFiles <- length(t1Files)
    if (length(InvTimes) != nFiles) 
        stop("readIRData: t1Files and InvTimes have different lengths")
    sdim <- dim(readNIfTI(t1Files[1], read_data = FALSE))
    s1 <- (1:3)[segmCodes == "CSF"]
    s2 <- (1:3)[segmCodes == "GM"]
    s3 <- (1:3)[segmCodes == "WM"]
    segm <- c1 <- readNIfTI(segmFile[1])@.Data
    if (length(segmFile) == 1) {
        segm[c1 == s1] <- 1
        segm[c1 == s2] <- 2
        segm[c1 == s3] <- 3
    }
    else if (length(segmFile) == 3) {
        c2 <- readNIfTI(segmFile[2], reorient = FALSE)
        c3 <- readNIfTI(segmFile[3], reorient = FALSE)
        segm[c1 >= pmax(1/3, c1, c2, c3)] <- s2
        segm[c2 >= pmax(1/3, c1, c2, c3)] <- s3
        segm[c3 >= pmax(1/3, c1, c2, c3)] <- s1
    }
    if (any(dim(segm) != sdim)) 
        stop("readIRData: dimensions of t1Files and segmFiles are incompatible")
    IRdata <- array(0, c(nfiles, sdim))
    for (i in 1:nfiles) IRdata[i, , , ] <- readNIfTI(t1Files[i], 
        reorient = FALSE)
    InvTimes[is.inf(InvTimes)] <- 10 * max(InvTimes[!is.inf(InvTimes)])
    if (is.null(sigma)) {
        ind <- (InvTimes == max(InvTimes))[1]
        ddata <- IRdata[ind, , , ]
        shat <- awsLocalSigma(ddata, steps = 16, mask = (segm == 
            1), ncoils = L, hsig = 2.5, lambda = 6, family = "Gauss")$sigma
        dim(shat) <- sdim
        shat <- shat[segm == 1]
        sigma <- median(shat)
    }
    data <- list(IRimg = IRdata, InvTimes = InvTimes, segm, sigma = sigma, 
        L = 1)
    class(data) <- "IRdata"
    data
  }