# Prepare IRMRI dataset The function reads IRMRI images given as NIfTI files in t1Files, inversion times and segmentation image(s) aund prepares an object class `"IRdata"` ```r readIRData(t1Files, InvTimes, segmFile, sigma = NULL, L = 1, segmCodes = c("GM", "WM", "CSF")) ``` ## 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 ## Returns 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 ## References J. Polzehl and K. Tabelow (2023), Magnetic Resonance Brain Imaging: Modeling and Data Analysis Using R, 2nd Edition, Chapter 7, Springer, Use R! Series. . J. Polzehl and K. Tabelow (2023), Magnetic Resonance Brain Imaging - Modeling and Data Analysis Using R: Code and Data. . ## Author(s) Karsten Tabelow tabelow@wias-berlin.de J\"org Polzehl polzehl@wias-berlin.de ## See Also `estimateIRfluid`, `estimateIRsolid`, `estimateIR`,`smoothIRSolid` ## Examples ```r dataDir0 <- system.file("extdataIR", package = "qMRI") dataDir <- tempdir() library(oro.nifti) library(qMRI) segm <- readNIfTI(file.path(dataDir0,"Brainweb_segm")) Sf <- 900 Rf <- 0.000285 Sgm <- 400 Rgm <- 0.00075 fgm <- .15 Swm <- 370 Rwm <- 0.0011 fwm <- .05 InvTimes0 <- c(100, 200, 400, 600, 800, 1200, 1600, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 6000, 15000) nTimes <- length(InvTimes0) sigma <- 40 ## generate IR signal IRdata <- generateIRData(segm, c(Sf,Rf), c(fgm,Rgm,Sgm), c(fwm,Rwm,Swm), InvTimes0, sigma) for(i in 1:9) writeNIfTI(as.nifti(IRdata[i,,,]), file.path(dataDir,paste0("IR0",i))) for(i in 10:nTimes) writeNIfTI(as.nifti(IRdata[i,,,]), file.path(dataDir,paste0("IR",i))) ## generate IRdata object t1Files <- list.files(dataDir,"*.nii.gz",full.names=TRUE) segmFile <- file.path(dataDir0,"Brainweb_segm") IRdata <- readIRData(t1Files, InvTimes0, segmFile, sigma=sigma, L=1, segmCodes=c("CSF","GM","WM")) ```

readIRData function