estimateESTATICS: Estimate parameters in the ESTATICS model.

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

Estimate parameters in the ESTATICS model.

Description

Evaluation of the ESTATICS model (Weisskopf (2013) using nonlinear least squares regression and a quasi-likelihood approach assuming a noncentral chi- or a Rician distribuion for the data. The latter should be preferred in case of low SNR (high resolution) data to avoid biased parameter estimates. Quasi-likelihood estimation requires a specification of the scale parameter sigma of the data distribution.

Usage

estimateESTATICS(mpmdata, TEScale = 100, dataScale = 1000, method = c("NLR", "QL"),
                 sigma = NULL, L = 1, maxR2star = 50,
                 varest = c("RSS", "data"), verbose = TRUE)

Arguments

mpmdata	Object of class MPMData as created by readMPMData.
TEScale	scale factor for TE (used for improved numerical stability)
dataScale	scale factor for image intensities (used for improved numerical stability)
method	either "NLR" or "QL". Specifies non-linear regression or quasi-likelihood.
sigma	scale parameter sigma of signal distribution (either a scalar or a 3D array). (only needed in case of method="QL".)
L	effective number of receiver coils (2*L is degrees of freedom of the signal distribution). L=1 for Rician distribution. (only needed in case of method="QL".)
maxR2star	maximum value allowed for the R2star parameter in the ESTATICS model.
varest	For parameter covariance estimation use either residual sum of squares (RSS) or estimate variances for T1, MT (is available) and PD from higest intensity images using function awsLocalSigmafrom package aws.
verbose	logical: Monitor process.

Value

list with components

modelCoeff	Estimated parameter maps
invCov	map of inverse covariance matrices
rsigma	map of residual standard deviations
isConv	convergence indicator map
isThresh	logical map indicating where R2star==maxR2star.
sdim	image dimension
nFiles	number of images
t1Files	vector of T1 filenames
pdFiles	vector of PD filenames
mtFiles	vector of MT filenames
model	model used (depends on specification of MT files)
maskFile	filename of brain mask
mask	brain mask
sigma	sigma
L	L
TR	TR values
TE	TE values
FA	Flip angles (FA)
TEScale	TEScale
dataScale	dataScale

and class-attribute 'ESTATICSModel'

Author(s)

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

References

Weiskopf, N.; Suckling, J.; Williams, G.; Correia, M. M.; Inkster, B.; Tait, R.; Ooi, C.; Bullmore, E. T. & Lutti, A. Quantitative multi-parameter mapping of R1, PD(*), MT, and R2(*) at 3T: a multi-center validation. Front Neurosci, Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, UK., 2013, 7, 95

J. Polzehl, K. Tabelow (2019). Magnetic Resonance Brain Imaging: Modeling and Data Analysis Using R. Springer, Use R! series. Doi:10.1007/978-3-030-29184-6.

See Also

readMPMData, calculateQI, smoothESTATICS, writeESTATICS, awsLocalSigma

Examples

dataDir <- system.file("extdata",package="qMRI")
#
#  set file names for T1w, MTw and PDw images
#
t1Names <- paste0("t1w_",1:8,".nii.gz")
mtNames <- paste0("mtw_",1:6,".nii.gz")
pdNames <- paste0("pdw_",1:8,".nii.gz")
t1Files <- file.path(dataDir, t1Names)
mtFiles <- file.path(dataDir, mtNames)
pdFiles <- file.path(dataDir, pdNames)
#
#  file names of mask and B1 field map
#
B1File <- file.path(dataDir, "B1map.nii.gz")
maskFile <- file.path(dataDir, "mask0.nii.gz")
#
#  Acquisition parameters (TE, TR, Flip Angle) for T1w, MTw and PDw images
#
TE <- c(2.3, 4.6, 6.9, 9.2, 11.5, 13.8, 16.1, 18.4,
        2.3, 4.6, 6.9, 9.2, 11.5, 13.8,
        2.3, 4.6, 6.9, 9.2, 11.5, 13.8, 16.1, 18.4)
TR <- rep(25, 22)
FA <- c(rep(21, 8), rep(6, 6), rep(6, 8))
#
#   read MPM example data
#
library(qMRI)
mpm <- readMPMData(t1Files, pdFiles, mtFiles,
                   maskFile, TR = TR, TE = TE,
                   FA = FA, verbose = FALSE)
#
#  Estimate Parameters in the ESTATICS model
#
modelMPM <- estimateESTATICS(mpm, method = "NLR")
# Alternatively using Quasi-Likelihood
sigma <- 50
modelMPMQL <- estimateESTATICS(mpm, method = "QL",
                  sigma = array(sigma,mpm$sdim), L = 1)

qMRI documentation built on Sept. 18, 2023, 9:08 a.m.