Estimate parameters in Inversion Recovery MRI experiments model for CSF voxel
The Inversion Recovery MRI signal in voxel containing only CSF follows is modeled as dependings on two parameters. These parameters are assumed to be tissue (and scanner) dependent.
estimateIRfluid(IRdataobj, TEScale = 100, dataScale = 1000, method = c("NLR", "QL"), varest = c("RSS", "data"), verbose = TRUE, lower = c(0, 0), upper = c(2, 2))
IRdataobj: Object of class "IRdata" as generated by function readIRData.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 sigmaif 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.verbose: Logical. Provide some runtime diagnostics.lower: Lower bounds for parameter values.upper: Upper bounds for parameter values.The Inversion Recovery MRI signal in voxel containing only CSF follows is modeled as dependings on two parameters. These parameters are assumed to be tissue (and scanner) dependent.
List of class IRfluid 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
Sf: Global estimate of maximal fluid signal
Rf: Global estimate of fluid relaxation rate
Sx: Array of maximal signals
Rx: Array of relaxation rates
sigma: Array of provided or estimated noise standard deviations
Convx: Array of convergence indicators
method: "NLS" for nonlinear regression or "QL" for quasi likelihood.
varest: Method used for variance estimation
The arrays only contain entries for fluid voxel.
J. Polzehl and K. Tabelow (2023), Magnetic Resonance Brain Imaging: Modeling and Data Analysis Using R, 2nd Edition, Chapter 7, Springer, Use R! Series. doi:10.1007/978-3-031-38949-8_7.
J. Polzehl and K. Tabelow (2023), Magnetic Resonance Brain Imaging - Modeling and Data Analysis Using R: Code and Data. doi:10.20347/WIAS.DATA.6.
Karsten Tabelow tabelow@wias-berlin.de
J"org Polzehl polzehl@wias-berlin.de
estimateIR, estimateIRsolid, estimateIRsolidfixed,smoothIRSolid
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 fluid setCores(2) # parallel mode using 2 threads IRfluid <- estimateIRfluid(IRdata, method="NLR", verbose=FALSE) cat("Estimated parameters Sf:", IRfluid$Sf, " Rf:", IRfluid$Rf, "\n")