smoothESTATICS: Adaptive smoothing of ESTATICS parameters and MPM data

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

Adaptive smoothing of ESTATICS parameters and MPM data

Description

Performs adaptive smoothing of parameter maps in the ESTATICS model and if mpmData is specified these data. Implements both vectorized variants of the Adaptive Weights Smoothing (AWS, Polzehl and Spokoiny (2006)) and patchwise AWS (PAWS, Polzehl et al (2018)) algorithms with weighting schemes determined by the estimated parameter maps and their covariances.

Usage

smoothESTATICS(mpmESTATICSModel, mpmData = NULL, kstar = 16, alpha = 0.025,
               patchsize = 0, mscbw =5, wghts = NULL, verbose = TRUE)

Arguments

mpmESTATICSModel	Object of class 'ESTATICSModel' as returned from function estimateESTATICS.
mpmData	(optional) Object of class MPMData as created by readMPMData from which the parameter maps were obtained.
kstar	Maximum number of steps.
alpha	specifies the scale parameter for the adaptation criterion. smaller values are more restrictive.
patchsize	Patchsize in PAWS, 0 corresponds to AWS, alternative values are 1 and 2.
mscbw	bandwidth for 3D median smoother used to stabilize the covariance estimates.
wghts	(optional) voxel size if measurments are not isotropic.
verbose	logical - provide information on progress

Value

list with components

modelCoeff	Estimated parameter maps
invCov	map of inverse covariance matrices
isConv	convergence indicator map
bi	Sum of weights map from AWS/PAWS
smoothPar	smooting parameters used in AWS/PAWS
smoothedData	smoothed mpmData
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 'sESTATICSModel'

Author(s)

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

References

J. Polzehl, V. Spokoiny, Propagation-separation approach for local likelihood estimation, Probab. Theory Related Fields 135 (3), (2006) , pp. 335–362.

J. Polzehl, K. Papafitsorus, K. Tabelow (2018). Patch-wise adaptive weights smoothing. WIAS-Preprint 2520.

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, estimateESTATICS

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, "mask.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")
#
#  smooth maps of ESTATICS Parameters
#
setCores(2)
modelMPMsp1 <- smoothESTATICS(modelMPM,
                              kstar = 16,
                              alpha = 0.004,
                              patchsize=1,
                              verbose = TRUE)
#
#  resulting ESTATICS parameter maps for central coronal slice
#
if(require(adimpro)){
rimage.options(zquantiles=c(.01,.99), ylab="z")
oldpar <- par(mfrow=c(2,4),mar=c(3,3,3,1),mgp=c(2,1,0))
pnames <- c("T1","MT","PD","R2star")
modelCoeff <- extract(modelMPM,"modelCoeff")
for(i in 1:4){
   rimage(modelCoeff[i,,11,])
   title(pnames[i])
   }
modelCoeff <- extract(modelMPMsp1,"modelCoeff")
for(i in 1:4){
   rimage(modelCoeff[i,,11,])
   title(paste("smoothed",pnames[i]))
   }
}
par(oldpar)

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