estimateIRsolidfixed function

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

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

Reestimate proportion of fluid with Sx and Rx fixed after smoothing.

estimateIRsolidfixed(IRmixedobj, TEScale = 100, dataScale = 1000,
verbose = TRUE, lower = c(0), upper = c(0.95))

Arguments

  • IRmixedobj: Object of class "IRmixed" as generated by function smoothIRSolid or estimateIRsolid.
  • 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 bound for fx (fluid proportion)
  • upper: upper bound for fx (fluid proportion)

Returns

List of class "IRmixed" 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, estimateIRsolid, estimateIR,smoothIRSolid, readIRData

Examples

## runs about 11 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)
## smoothing 
sIRmix <- smoothIRSolid(IRmix, alpha=1e-4, partial=FALSE, verbose=FALSE)
## reestimate
sIRmix <- estimateIRsolidfixed(sIRmix, verbose=FALSE)
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