estimateIR function

Estimate IRMRI parameters

Estimate IRMRI parameters

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

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.

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. 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, 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")
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