Obtain quantitative maps from estimated ESTATICS parameters.

Quantitaive imaging parameters are calculated from the estimated parameters in the ESTATICS model. This involves a correction for magnetic field inhomogeneities if the information is provided in argument b1File and use of a second of a second recovery delay TR2 in case of Dual-Exitation FLASH measurements (Helms 2008).

calculateQI(mpmESTATICSModel, b1File = NULL, TR2 = 0, verbose = TRUE)

Arguments

mpmESTATICSModel	Object of class 'ESTATICSModel' as returned from function `estimateESTATICS`.
b1File	(optional) Name of a file containing a B1-field inhomogeneity map (.nii)
TR2	second recovery delay TR2 in case of Dual-Exitation FLASH measurements.
verbose	logical: Monitor process.

Value

List with components

b1Map

Estimated map of R1

R2star

Estimated map of R2star

Estimated map of PD

Estimated map of delta (if MT-series was used)

model

Type of ESTATICS model used

t1Files

filenames T1

mtFiles

filenames MT

pdFiles

filenames PD

mask

brainmask

and class-attribute 'qMaps' .

References

Helms, G.; Dathe, H.; Kallenberg, K. & Dechent, P. High-Resolution Maps of Magnetization Transfer with Inherent Correction for RF Inhomogeneity and T1 Relaxation Obtained from 3D FLASH MRI Magn. Res. Med., 2008, 60, 1396-1407

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.

Author

Karsten Tabelow tabelow@wias-berlin.de
J\"org Polzehl polzehl@wias-berlin.de

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)
#
# limit calculations to voxel in the central coronal slice
# to reduce execution time of the example
#
#
#  Estimate Parameters in the ESTATICS model
#
modelMPM <- estimateESTATICS(mpm, method = "NLR")
#> Design of the model:
#>       [,1] [,2] [,3]  [,4]
#>  [1,]    1    0    0 0.023
#>  [2,]    1    0    0 0.046
#>  [3,]    1    0    0 0.069
#>  [4,]    1    0    0 0.092
#>  [5,]    1    0    0 0.115
#>  [6,]    1    0    0 0.138
#>  [7,]    1    0    0 0.161
#>  [8,]    1    0    0 0.184
#>  [9,]    0    1    0 0.023
#> [10,]    0    1    0 0.046
#> [11,]    0    1    0 0.069
#> [12,]    0    1    0 0.092
#> [13,]    0    1    0 0.115
#> [14,]    0    1    0 0.138
#> [15,]    0    0    1 0.023
#> [16,]    0    0    1 0.046
#> [17,]    0    0    1 0.069
#> [18,]    0    0    1 0.092
#> [19,]    0    0    1 0.115
#> [20,]    0    0    1 0.138
#> [21,]    0    0    1 0.161
#> [22,]    0    0    1 0.184
#>  done
#> Start estimation in 1600 voxel at 2021-05-17 15:22:09 
#> 
  |                                                                            
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  |============================================                          |  62%
#> Finished estimation 2021-05-17 15:22:12 
#
#  resulting ESTATICS parameter maps for central coronal slice
#
if(require(adimpro)){
rimage.options(zquantiles=c(.01,.99), ylab="z")
oldpar <- par(mfrow=c(2,2),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])
   }
}
#> Loading required package: adimpro
#> Reading RAW images requires to install dcraw, see 
#> 
#>     http://cybercom.net/~dcoffin/dcraw/ for LINUX and http://www.insflug.org/raw/
#>     for MAC OS and Windows 
#> Use awsMethods::setCores(ncores) to specify number of cores for openMP

#
#  Compute quantitative maps (R1, R2star, PD, MT)
#
qMRIMaps <- calculateQI(modelMPM,
                        b1File = B1File,
                        TR2 = 3.4)
#> reading B1 correction file
#> calculating R1 ... Spoiling correction ... v3* 0.8mm R4 protocol
#> Spoiling correction ... v3* 0.8mm R4 protocol
#> done
#> calculating PD ... done
#> calculating MT ... done
#
#  resulting quantitative maps for central coronal slice
#
if(require(adimpro)){
rimage.options(zquantiles=c(.01,.99), ylab="z")
par(mfrow=c(2,2),mar=c(3,3,3,1),mgp=c(2,1,0))
nmaps <- c("R1","R2star","PD","MT")
qmap <- extract(qMRIMaps,nmaps)
for (i in 1:4){
   rimage(qmap[[i]][,11,],main=nmaps[i])
}
par(oldpar)
}