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)
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.List with components - 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
and class-attribute 'qMaps' .
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.
Karsten Tabelow tabelow@wias-berlin.de
J"org Polzehl polzehl@wias-berlin.de
readMPMData, estimateESTATICS, smoothESTATICS, writeESTATICS, awsLocalSigma
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) }