Methods for Quantitative Magnetic Resonance Imaging ('qMRI')

Description

Implementation of methods for estimation of quantitative maps from Multi-Parameter Mapping (MPM) acquisitions (Weiskopf et al. (2013) <doi:10.3389/fnins.2013.00095>) and analysis of Inversion Recovery MRI data. Usage of the package is described in Polzehl and Tabelow (2023), "Magnetic Resonance Brain Imaging", 2nd Edition, Chapter 6 and 7, Springer, Use R! Series. <doi:10.1007/978-3-031-38949-8>. 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> provides extensive example code and data.

Details

The DESCRIPTION file:

Index of help topics:

MREdisplacement         Calculate the motion induced signal phase for
                        IR-MRE in biphasic material
awssigmc                Estimate noise variance for multicoil MR
                        systems
calculateQI             Obtain quantitative maps from estimated
                        ESTATICS parameters.
colMT                   MT map color scheme
estimateESTATICS        Estimate parameters in the ESTATICS model.
estimateIR              Estimate IRMRI parameters
estimateIRfluid         Estimate parameters in Inversion Recovery MRI
                        experiments model for CSF voxel
estimateIRsolid         Estimate parameters in Inversion Recovery MRI
                        experiments mixture model for non-fluid voxel
estimateIRsolidfixed    Estimate mixture parameter in Inversion
                        Recovery MRI experiments mixture model for
                        non-fluid voxel
extract.ANY-method      Methods to extract information from objects of
                        class '"MPMData"', '"ESTATICSModel"',
                        '"sESTATICSModel"', '"qMaps"', '"IRdata"',
                        '"IRfluid"' and '"IRmixed"'.
generateIRData          generate IR MRI example data
qMRI-package            Methods for Quantitative Magnetic Resonance
                        Imaging ('qMRI')
readIRData              Prepare IRMRI dataset
readMPMData             Read experimental Multi-Parameter Mapping (MPM)
                        data.
smoothESTATICS          Adaptive smoothing of ESTATICS parameters and
                        MPM data
smoothIRSolid           Smooth object generated by function
                        'estimateIRsolid'
writeESTATICS           Write maps of ESTATICS parameters in
                        standardized form as NIfTI files.
writeQI                 Write estimated maps in standardized form as
                        NIfTI files.

Further information is available in the following vignettes:

Author(s)

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

Maintainer: Karsten Tabelow <karsten.tabelow@wias-berlin.de>

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 and K. Tabelow (2023), Magnetic Resonance Brain Imaging: Modeling and Data Analysis Using R, 2nd Edition, Chapter 6 and 7, Springer, Use R! Series. <doi:10.1007/978-3-031-38949-8>.

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>.

See Also

aws

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))
on.exit(par(oldpar))
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]))
   }
}
#
#  Compute quantitative maps (R1, R2star, PD, MT)
#
qMRIMaps <- calculateQI(modelMPM,
                        b1File = B1File,
                        TR2 = 3.4)
qMRISmoothedp1Maps <- calculateQI(modelMPMsp1,
                                    b1File = B1File,
                                    TR2 = 3.4)
#
#  resulting quantitative maps for central coronal slice
#
if(require(adimpro)){
rimage.options(zquantiles=c(.01,.99), ylab="z")
par(mfrow=c(2,4),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])
qmap <- extract(qMRISmoothedp1Maps,nmaps)
for (i in 1:4) rimage(qmap[[i]][,11,],main=paste("Smoothed",nmaps[i]))
}
par(oldpar)

Calculate the motion induced signal phase for IR-MRE in biphasic material

Description

The function takes magnitude images and phase images (as NIfTI files) recordet with inversion IT1=Inf and a second inversion time IT2 that nulls the fluid signal. Tissue parameters (Relaxation rates) are extracted from an object of class "IRmixed" calculated from data of a related IRMRI experiment.

Usage

MREdisplacement(MagnFiles1, PhaseFiles1, MagnFiles2, PhaseFiles2, TI2 = 2400,
                IRmixobj, method = c("full", "approx"),rescale=FALSE,verbose=FALSE)

Arguments

Details

The first 4 arguments need to be vectors of filenames of identical length with files containing compatible 3D NIfTI images. Object IRmixobj needs to contain a components segm and Rx of compatible dimension that need to be registered to the MRE images.

Value

A list of class "IRMREbiphasic" with components

Author(s)

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

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>.

See Also

estimateIRfluid, estimateIRsolid, estimateIRsolidfixed,smoothIRSolid

Estimate noise variance for multicoil MR systems

Description

The distribution of image intensity values S_i divided by the noise standard deviation in K-space \sigma in dMRI experiments is assumed to follow a non-central chi-distribution with 2L degrees of freedom and noncentrality parameter \eta, where L refers to the number of receiver coils in the system and \sigma \eta is the signal of interest. This is an idealization in the sense that each coil is assumed to have the same contribution at each location. For realistic modeling L should be a locally smooth function in voxel space that reflects the varying local influence of the receiver coils in the the reconstruction algorithm used.

The functions assume L to be known and estimate either a local (function awslsigmc) or global ( function awssigmc) \sigma employing an assumption of local homogeneity for the noncentrality parameter \eta.

Function afsigmc implements estimates from Aja-Fernandez (2009). Function aflsigmc implements the estimate from Aja-Fernandez (2013).

Usage

awssigmc(y, steps, mask = NULL, ncoils = 1, vext = c(1, 1), lambda = 20, 
         h0 = 2, verbose = FALSE, sequence = FALSE, hadj = 1, q = 0.25, 
         qni = .8, method=c("VAR","MAD"))
awslsigmc(y, steps, mask = NULL, ncoils = 1, vext = c(1, 1), lambda = 5, minni = 2, 
         hsig = 5, sigma = NULL, family = c("NCchi"), verbose = FALSE, 
         trace=FALSE, u=NULL)

Arguments

Value

a list with components

Author(s)

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

References

K. Tabelow, H.U. Voss, J. Polzehl, Local estimation of the noise level in MRI using structural adaptation, Medical Image Analysis, 20 (2015), pp. 76–86.

See Also

aws

Obtain quantitative maps from estimated ESTATICS parameters.

Description

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).

Usage

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

Arguments

Value

List with components

and class-attribute 'qMaps' .

Author(s)

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

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 and K. Tabelow (2023), Magnetic Resonance Brain Imaging: Modeling and Data Analysis Using R, 2nd Edition, Chapter 6, Springer, Use R! Series. <doi:10.1007/978-3-031-38949-8_6>.

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>.

See Also

readMPMData, estimateESTATICS, 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)
#
# 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")
#
#  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))
on.exit(par(oldpar))
pnames <- c("T1","MT","PD","R2star")
modelCoeff <- extract(modelMPM,"modelCoeff")
for(i in 1:4){
   rimage(modelCoeff[i,,11,])
   title(pnames[i])
   }
}
#
#  Compute quantitative maps (R1, R2star, PD, MT)
#
qMRIMaps <- calculateQI(modelMPM,
                        b1File = B1File,
                        TR2 = 3.4)
#
#  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)
}

MT map color scheme

Description

Color map implementing the color scheme for MT maps. This is the plasma scale from Matplotlib (pyplot) generated by function plasma from package viridisLite.

Usage

colMT

Format

A vector with 256 RGB color values.

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

Value

list with components

and class-attribute 'ESTATICSModel'

Author(s)

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

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 and K. Tabelow (2023), Magnetic Resonance Brain Imaging: Modeling and Data Analysis Using R, 2nd Edition, Chapter 6, Springer, Use R! Series. <doi:10.1007/978-3-031-38949-8_6>.

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>.

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)

Estimate IRMRI parameters

Description

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

Usage

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

Details

This function implements the complete pipeline of IRMRI anlysis.

Value

List of class "IRmixed" with components

The arrays contain entries for all voxel with segments%in%1:3.

Author(s)

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

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>.

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")

Estimate parameters in Inversion Recovery MRI experiments model for CSF voxel

Description

The Inversion Recovery MRI signal in voxel containing only CSF follows is modeled as $S_InvTime = par[1] * abs( 1 - 2 * exp(-InvTime*par[2]) )$ dependings on two parameters. These parameters are assumed to be tissue (and scanner) dependent.

Usage

estimateIRfluid(IRdataobj, TEScale = 100, dataScale = 1000,
method = c("NLR", "QL"), varest = c("RSS", "data"),
verbose = TRUE, lower = c(0, 0), upper = c(2, 2))

Arguments

Details

The Inversion Recovery MRI signal in voxel containing only CSF follows is modeled as $S_InvTime = par[1] * abs( 1 - 2 * exp(-InvTime*par[2]) )$ dependings on two parameters. These parameters are assumed to be tissue (and scanner) dependent.

Value

List of class IRfluid with components

The arrays only contain entries for fluid voxel.

Author(s)

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

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>.

See Also

estimateIR, estimateIRsolid, estimateIRsolidfixed,smoothIRSolid

Examples

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

Description

The Inversion Recovery MRI signal in non-fluid voxel follows is modeled as a mixture of a fluid and a solid compartment.

Usage

estimateIRsolid(IRfluidobj, TEScale = 100, dataScale = 1000,
verbose = TRUE, lower = c(0, 0, 0), upper = c(0.95, 2, 2))

Arguments

Details

The Inversion Recovery MRI signal in non-fluid voxel follows is modeled as a mixture of a fluid and a solid compartment. The function calculates estimates of the maximum signal and recovery rate for the solid compartment and a mixture coefficient (proportion of fluid) for all voxel with segment%in%2:3using results from function estimateIRfluid.

Value

List of class IRmixed with components

The arrays contain entries for all voxel with segments%in%1:3.

Author(s)

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

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>.

See Also

estimateIRfluid, estimateIR, estimateIRsolidfixed,smoothIRSolid

Examples

## runs about 7 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)

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

Description

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

Usage

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

Arguments

Value

List of class "IRmixed" components

The arrays contain entries for all voxel with segments%in%1:3.

Author(s)

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

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>.

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)

Methods to extract information from objects of class "MPMData", "ESTATICSModel", "sESTATICSModel", "qMaps", "IRdata", "IRfluid" and "IRmixed".

Description

The extract-methods extract and/or compute specified statistics from object of class "MPMData", "ESTATICSModel", "sESTATICSModel", "qMaps", "IRdata", "IRfluid" and "IRmixed". The [-methods can be used to reduce objects of class "MPMData", "ESTATICSModel", "sESTATICSModel", "qMaps", "IRdata", "IRfluid" and "IRmixed" such that they contain a subcube of data and results.

Usage

## S3 method for class 'MPMData'
extract(x, what, ...)
## S3 method for class 'ESTATICSModel'
extract(x, what, ...)
## S3 method for class 'sESTATICSModel'
extract(x, what, ...)
## S3 method for class 'qMaps'
extract(x, what, ...)
## S3 method for class 'IRdata'
extract(x, what, ...)
## S3 method for class 'IRfluid'
extract(x, what, ...)
## S3 method for class 'IRmixed'
extract(x, what, ...)
## S3 method for class 'MPMData'
x[i, j, k, ...]
## S3 method for class 'ESTATICSModel'
x[i, j, k, ...]
## S3 method for class 'sESTATICSModel'
x[i, j, k, ...]
## S3 method for class 'qMaps'
x[i, j, k, ...]
## S3 method for class 'IRdata'
x[i, j, k, tind, ...]
## S3 method for class 'IRfluid'
x[i, j, k, ...]
## S3 method for class 'IRmixed'
x[i, j, k, ...]

Arguments

Value

A list with components carrying the names of the options specified in argument what.

Methods

class(x) = "ANY"

Returns a warning for extract

class(x) = "MPMData"

Depending the occurence of names in what a list with the specified components is returned

• ddata: mpm data

• sdim: dimension of image cube

• nFiles: number of images / image files

• t1Files: character - filenames of t1Files

• pdFiles: character - filenames of pdFiles

• mtFiles: character - filenames of mtFiles

• model: Number of the ESTATICS model that can be used

• maskFile: character - filenames of maskFile

• mask: mask

• TR: vector of TR values

• TE: vector of TE values

• FA: vector of FA values

class(x) = "ESTATICSModel"

Depending the occurence of names in what a list with the specified components is returned

• 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: standard deviation sigma

• L: effective number of receiver coils L

• TR: TR values

• TE: TE values

• FA: Flip angles (FA)

• TEScale: TEScale

• dataScale: dataScale

class(x) = "sESTATICSModel"

Depending the occurence of names in what a list with the specified components is returned

• modelCoeff: Estimated parameter maps

• invCov: map of inverse covariance matrices

• rsigma: map of residual standard deviations

• isConv: convergence indicator map

• bi: Sum of weights map from AWS/PAWS

• smoothPar: smooting parameters used in AWS/PAWS

• smoothedData: smoothed mpmData

• 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: effective number of receiver coils L

• TR: TR values

• TE: TE values

• FA: Flip angles (FA)

• TEScale: TEScale

• dataScale: dataScale

class(x) = "qMaps"

Depending the occurence of names in what a list with the specified components is returned

• b1Map: b1Map

• R1: Estimated map of R1

• R2star: Estimated map of R2star

• PD: Estimated map of PD

• MT: 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

Author(s)

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

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>.

generate IR MRI example data

Description

The function generates IR MRI example data for specified parameters

Usage

generateIRData(segm, pCSF, pGM, pWM, InvTimes, sigma = 40)

Arguments

Value

array with dimension c(nTimes,dim(segm))

Note

used in examples for IR functions

Author(s)

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

See Also

estimateIRfluid, estimateIRsolid, estimateIR,smoothIRSolid, readIRData

Prepare IRMRI dataset

Description

The function reads IRMRI images given as NIfTI files in t1Files, inversion times and segmentation image(s) aund prepares an object class "IRdata"

Usage

readIRData(t1Files, InvTimes, segmFile, sigma = NULL, L = 1,
           segmCodes = c("GM", "WM", "CSF"))

Arguments

Value

A list of class "IRdata" with components

Author(s)

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

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>.

See Also

estimateIRfluid, estimateIRsolid, estimateIR,smoothIRSolid

Examples

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"))

Read experimental Multi-Parameter Mapping (MPM) data.

Description

The function reads data generated in Multimodal Parameter Mapping (MPM) experiments.

Usage

readMPMData(t1Files = NULL, pdFiles = NULL, mtFiles = NULL, maskFile = NULL,
            TR = NULL, TE = NULL, FA = NULL, wghts = NULL, verbose = TRUE)

Arguments

Value

List with components

and class-attribute 'mpmData'

Author(s)

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

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 and K. Tabelow (2023), Magnetic Resonance Brain Imaging: Modeling and Data Analysis Using R, 2nd Edition, Chapter 6, Springer, Use R! Series. <doi:10.1007/978-3-031-38949-8_6>.

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>.

See Also

estimateESTATICS, 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, "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)

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

Value

list with components

and class-attribute 'sESTATICSModel'

Author(s)

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

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 and K. Tabelow (2023), Magnetic Resonance Brain Imaging: Modeling and Data Analysis Using R, 2nd Edition, Chapter 6, Springer, Use R! Series. <doi:10.1007/978-3-031-38949-8_6>.

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>.

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))
on.exit(par(oldpar))
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)

Smooth object generated by function estimateIRsolid

Description

Adaptive smoothing of Rx and Sx maps over WM and GM areas.

Usage

smoothIRSolid(IRmixedobj, kstar = 24, patchsize = 1, alpha = 0.025,
                   mscbw = 5, bysegment=TRUE, partial=TRUE, verbose=TRUE)

Arguments

Details

This uses a vectorized version of the AWS algorithm that emloys inverse covariance estimates of the estimated parameters. Local smoothing is done for Rx and Sx maps in ergs which can be assumed to be locally smooth within tissue. No smoothing for fx maps since they may vary.

Value

an object of class "IRmixed", but with components Sx and Rx replaced. The object carries an additional component bi containing an array of sum of weights characterizing the amount of smoothing.

Author(s)

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

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>.

See Also

estimateIRfluid, estimateIRsolid, estimateIRsolidfixed,estimateIR

Examples

## runs about 10 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)

Write maps of ESTATICS parameters in standardized form as NIfTI files.

Description

R2, ST1, SPD and, if available, SMT-maps are written as compressed NIfTI files into directory the speecified directory. If class(mpmESTATICSModel) == "sESTATICSModel" and an smoothed data are stored in mpmESTATICSModel$smoothedData the smoothed data are stored as ompressed NIfTI files in dir with filenames assembled using prefix and the names of the data source files.

Usage

writeESTATICS(mpmESTATICSModel, dir = NULL, prefix = "estatics", verbose = TRUE)

Arguments

Value

The function returns NULL

Author(s)

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

References

J. Polzehl and K. Tabelow (2023), Magnetic Resonance Brain Imaging: Modeling and Data Analysis Using R, 2nd Edition, Chapter 6, Springer, Use R! Series. <doi:10.1007/978-3-031-38949-8_6>.

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>.

See Also

readMPMData, estimateESTATICS, smoothESTATICS

Examples

dataDir <- system.file("extdata",package="qMRI")
outDir <- tempdir()
#
#  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")
#
#  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))
on.exit(par(oldpar))
pnames <- c("T1","MT","PD","R2star")
modelCoeff <- extract(modelMPM,"modelCoeff")
for(i in 1:4){
   rimage(modelCoeff[i,,11,])
   title(pnames[i])
   }
}
#
#  write ESTATICS parameter maps
#
writeESTATICS(modelMPM, dir=outDir, prefix="estatics")
par(oldpar)

Write estimated maps in standardized form as NIfTI files.

Description

Quantitative R2, R1, PD and, if available, MT-maps are written as compressed NIfTI files into directory the specified directory.

Usage

writeQI(qi, dir = NULL, prefix="qmap", verbose = TRUE)

Arguments

Value

The function returns NULL

Author(s)

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

References

J. Polzehl and K. Tabelow (2023), Magnetic Resonance Brain Imaging: Modeling and Data Analysis Using R, 2nd Edition, Chapter 6, Springer, Use R! Series. <doi:10.1007/978-3-031-38949-8_6>.

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>.

See Also

readMPMData, estimateESTATICS,calculateQI

Examples

dataDir <- system.file("extdata",package="qMRI")
outDir <- tempdir()
#
#  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")
#
#  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))
on.exit(par(oldpar))
pnames <- c("T1","MT","PD","R2star")
modelCoeff <- extract(modelMPM,"modelCoeff")
for(i in 1:4){
   rimage(modelCoeff[i,,11,])
   title(pnames[i])
   }
}
#
#  Compute quantitative maps (R1, R2star, PD, MT)
#
qMRIMaps <- calculateQI(modelMPM,
                        b1File = B1File,
                        TR2 = 3.4)
#
#  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])
}
#
#  write qmaps
#
writeQI(qMRIMaps, dir=outDir, prefix="qmap")
par(oldpar)