# Estimate IRMRI parameters Parameter estimation (intensity, relaxation rate, proportion of fluid) in Inversion Recovery MRI data. ```r estimateIR(IRdataobj, TEScale = 100, dataScale = 1000, method = c("NLR", "QL"), varest = c("RSS","data"), fixed = TRUE, smoothMethod=c("PAWS","Depth"), kstar = 24, alpha = .025, bysegment = TRUE, verbose = TRUE) ``` ## Arguments - `IRdataobj`: 4D array of IRMRI signals. First dimension corresponds to Inversion times (InvTime). - `TEScale`: Internal scale factor for Echo Times. This influences parameter scales in numerical calculations. - `dataScale`: Internal scale factor for MR signals. This influences parameter scales in numerical calculations. - `method`: Either `"NLS"` for nonlinear least squares (ignores Rician bias) or `"QL"` for Quasi-Likelihood. The second option is more accurate but requires additional information and is computationally more expensive. - `varest`: Method to, in case of `method="QR"`, estimate `sigma`if not provided. Either from residual sums of squares (`"RSS"`) or MR signals (`"data"`) using function `varest` from package `aws`. Only to be used in case that no image registration was needed as preprocessing. - `fixed`: Should adaptive smoothing performed for Sx and Rx maps and fx maps reestimated afterwards ? - `smoothMethod`: Either "PAWS" or "Depth". the second option is not yet implemented. - `kstar`: number of steps used in PAWS - `alpha`: significance level for decisions in aws algorithm (suggestion: between 1e-5 and 0.025) - `bysegment`: `TRUE`: restrict smoothing to segments from segmentation, `FALSE`: restrict smoothing to solid mask. - `verbose`: Logical. Provide some runtime diagnostics. ## Details This function implements the complete pipeline of IRMRI anlysis. ## Returns List of class `"IRmixed"` 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 - **fx**: Array of fluid proportions - **Sx**: Array of maximal signals - **Rx**: Array of relaxation rates - **Sf**: Global estimate of maximal fluid signal - **Rf**: Global estimate of fluid relaxation rate - **ICovx**: Covariance matrix of estimates `fx`, `Sx` and `Rx`. - **sigma**: Array of provided or estimated noise standard deviations - **Convx**: Array of convergence indicators - **rsdx**: Residual standard deviations The arrays contain entries for all voxel with `segments%in%1:3`. ## 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`, `estimateIRsolidfixed`,`smoothIRSolid` ## Examples ```r ## runs about 30 seconds 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")) ## estimate all sIRmix <- estimateIR(IRdata, method="QL") ```

estimateIR function