estimateIR: Estimate IRMRI parameters

In qMRI: Methods for Quantitative Magnetic Resonance Imaging ('qMRI')

Estimate IRMRI parameters

Description

Parameter estimation (intensity, relaxation rate, proportion of fluid) in Inversion Recovery MRI data.

Usage

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 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.
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.

Value

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.

Author(s)

Karsten Tabelow [e-mail]
J\"org Polzehl [e-mail]

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. <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>.

See Also

estimateIRfluid, estimateIRsolid, estimateIRsolidfixed,smoothIRSolid

Examples

## 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")

qMRI documentation built on April 3, 2025, 10:51 p.m.