estimateIRsolid function

Estimate parameters in Inversion Recovery MRI experiments mixture model for non-fluid voxel

The Inversion Recovery MRI signal in non-fluid voxel follows is modeled as a mixture of a fluid and a solid compartment.

estimateIRsolid(IRfluidobj, TEScale = 100, dataScale = 1000,
verbose = TRUE, lower = c(0, 0, 0), upper = c(0.95, 2, 2))

Arguments

• IRfluidobj: Object of class "IRfluid" as generated by function estimateIRfluid.
• 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.
• verbose: Logical. Provide some runtime diagnostics.
• lower: Lower bounds for parameter values.
• upper: Upper bounds for parameter values.

Details

The Inversion Recovery MRI signal in non-fluid voxel follows is modeled as a mixture of a fluid and a solid compartment. The function calculates estimates of the maximum signal and recovery rate for the solid compartment and a mixture coefficient (proportion of fluid) for all voxel with segment%in%2:3using results from function estimateIRfluid.

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

• method: "NLS" for nonlinear regression or "QL" for quasi likelihood.

• varest: Method used for variance estimation

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. 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.

Author(s)

Karsten Tabelow tabelow@wias-berlin.de

J"org Polzehl polzehl@wias-berlin.de

See Also

estimateIRfluid, estimateIR, estimateIRsolidfixed,smoothIRSolid

Examples

## runs about 7 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 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")
## estimate solid
IRmix <- estimateIRsolid(IRfluid, verbose=FALSE)