# qmt_spgr: quantitative Magnetizatoion Transfer (qMT) using Spoiled Gradient Echo (or FLASH)Ā¶

## Contents

% This m-file has been automatically generated using qMRgenBatch(qmt_spgr) % Command Line Interface (CLI) is well-suited for automatization % purposes and Octave. % % Please execute this m-file section by section to get familiar with batch % processing for qmt_spgr on CLI. % % Demo files are downloaded into qmt_spgr_data folder. % % Written by: Agah Karakuzu, 2017 % =========================================================================

## I- DESCRIPTION

qMRinfo('qmt_spgr'); % Describe the model

qmt_spgr: quantitative Magnetizatoion Transfer (qMT) using Spoiled Gradient Echo (or FLASH) a href="matlab: figure, imshow qmt_spgr.png ;"Pulse Sequence Diagram/a Assumptions: FILL Inputs: MTdata Magnetization Transfert data (R1map) 1/T1map (VFA RECOMMENDED Boudreau 2017 MRM) (B1map) B1 field map, used for flip angle correction (=1 if not provided) (B0map) B0 field map, used for offset correction (=0Hz if not provided) (Mask) Binary mask to accelerate the fitting Outputs: F Ratio of number of restricted pool to free pool, defined as F = M0r/M0f = kf/kr. kr Exchange rate from the free to the restricted pool (note that kf and kr are related to one another via the definition of F. Changing the value of kf will change kr accordingly, and vice versa). R1f Longitudinal relaxation rate of the free pool (R1f = 1/T1f). R1r Longitudinal relaxation rate of the restricted pool (R1r = 1/T1r). T2f Tranverse relaxation time of the free pool (T2f = 1/R2f). T2r Tranverse relaxation time of the restricted pool (T2r = 1/R2r). (kf) Exchange rate from the restricted to the free pool. (resnorm) Fitting residual. Protocol: MTdata Array [Nb of volumes x 2] Angle MT pulses angles (degree) Offset Offset frequencies (Hz) TimingTable Vector [5x1] Tmt Duration of the MT pulses (s) Ts Free precession delay between the MT and excitation pulses (s) Tp Duration of the excitation pulse (s) Tr Free precession delay after the excitation pulse, before the next MT pulse (s) TR Repetition time of the whole sequence (TR = Tmt + Ts + Tp + Tr) Options: MT Pulse Shape Shape of the MT pulse. Available shapes are: - hard - gaussian - gausshann (gaussian pulse with Hanning window) - sinc - sinchann (sinc pulse with Hanning window) - singauss (sinc pulse with gaussian window) - fermi Sinc TBW Time-bandwidth product for the sinc MT pulses (applicable to sinc, sincgauss, sinchann MT pulses). Bandwidth Bandwidth of the gaussian MT pulse (applicable to gaussian, gausshann and sincgauss MT pulses). Fermi transition (a) slope 'a' (related to the transition width) of the Fermi pulse (applicable to fermi MT pulse). Assuming pulse duration at 60 dB (from the Bernstein handbook) and t0 = 10a, slope = Tmt/33.81; # of MT pulses Number of pulses used to achieve steady-state before a readout is made. Fitting constraints Use R1map to By checking this box, you tell the fitting constrain R1f algorithm to check for an observed R1map and use its value to constrain R1f. Checking this box will automatically set the R1f fix box to true in the Fit parameters table. Fix R1r = R1f By checking this box, you tell the fitting algorithm to fix R1r equal to R1f. Checking this box will automatically set the R1r fix box to true in the Fit parameters table. Fix R1f*T2f By checking this box, you tell the fitting algorithm to compute T2f from R1f value. R1f*T2f value is set in the next box. R1f*T2f = Value of R1f*T2f (no units) Model Model you want to use for fitting. Available models are: - SledPikeRP (Sled Pike rectangular pulse), - SledPikeCW (Sled Pike continuous wave), - Yarkykh (Yarnykh Yuan) - Ramani Note: Sled Pike models will show different options than Yarnykh or Ramani. Lineshape The absorption lineshape of the restricted pool. Available lineshapes are: - Gaussian - Lorentzian - SuperLorentzian Read pulse alpha Flip angle of the excitation pulse. Compute SfTable By checking this box, you compute a new SfTable Command line usage: a href="matlab: qMRusage(qmt_spgr);"qMRusage(qmt_spgr/a Author: Ian Gagnon, 2017 References: Please cite the following if you use this module: Sled, J.G., Pike, G.B., 2000. Quantitative interpretation of magnetization transfer in spoiled gradient echo MRI sequences. J. Magn. Reson. 145, 24?36. In addition to citing the package: Karakuzu A., Boudreau M., Duval T.,Boshkovski T., Leppert I.R., Cabana J.F., Gagnon I., Beliveau P., Pike G.B., Cohen-Adad J., Stikov N. (2020), qMRLab: Quantitative MRI analysis, under one umbrella doi: 10.21105/joss.02343 Reference page in Doc Center doc qmt_spgr

## II- MODEL PARAMETERS

## a- create object

Model = qmt_spgr;

## b- modify options

|- This section will pop-up the options GUI. Close window to continue. |- Octave is not GUI compatible. Modify Model.options directly.

```
Model = Custom_OptionsGUI(Model); % You need to close GUI to move on.
```

## III- FIT EXPERIMENTAL DATASET

## a- load experimental data

|- qmt_spgr object needs 5 data input(s) to be assigned: |- MTdata |- R1map |- B1map |- B0map |- Mask

data = struct(); % MTdata.mat contains [88 128 1 10] data. load('qmt_spgr_data/MTdata.mat'); % R1map.mat contains [88 128] data. load('qmt_spgr_data/R1map.mat'); % B1map.mat contains [88 128] data. load('qmt_spgr_data/B1map.mat'); % B0map.mat contains [88 128] data. load('qmt_spgr_data/B0map.mat'); % Mask.mat contains [88 128] data. load('qmt_spgr_data/Mask.mat'); data.MTdata= double(MTdata); data.R1map= double(R1map); data.B1map= double(B1map); data.B0map= double(B0map); data.Mask= double(Mask);

## b- fit dataset

|- This section will fit data.

FitResults = FitData(data,Model,0);

=============== qMRLab::Fit ====================== Operation has been started: qmt_spgr Warning: No MToff (i.e. no volumes acquired with Angles=0) -- Fitting assumes that MTData are already normalized. Warning: No MToff (i.e. no volumes acquired with Angles=0) -- Fitting assumes that MTData are already normalized. Warning: No MToff (i.e. no volumes acquired with Angles=0) -- Fitting assumes that MTData are already normalized. Elapsed time is 2.358713 seconds. Operation has been completed: qmt_spgr ==================================================

## c- show fitting results

|- Output map will be displayed. |- If available, a graph will be displayed to show fitting in a voxel. |- To make documentation generation and our CI tests faster for this model, we used a subportion of the data (40X40X40) in our testing environment. |- Therefore, this example will use FitResults that comes with OSF data for display purposes. |- Users will get the whole dataset (384X336X224) and the script that uses it for demo via qMRgenBatch(qsm_sb) command.

```
FitResults_old = load('FitResults/FitResults.mat');
qMRshowOutput(FitResults_old,data,Model);
```

Warning: No MToff (i.e. no volumes acquired with Angles=0) -- Fitting assumes that MTData are already normalized.

## d- Save results

|- qMR maps are saved in NIFTI and in a structure FitResults.mat that can be loaded in qMRLab graphical user interface |- Model object stores all the options and protocol. It can be easily shared with collaborators to fit their own data or can be used for simulation.

```
FitResultsSave_nii(FitResults);
Model.saveObj('qmt_spgr_Demo.qmrlab.mat');
```

Warning: Directory already exists.

## V- SIMULATIONS

|- This section can be executed to run simulations for qmt_spgr.

## a- Single Voxel Curve

|- Simulates Single Voxel curves: (1) use equation to generate synthetic MRI data (2) add rician noise (3) fit and plot curve

x = struct; x.F = 0.16; x.kr = 30; x.R1f = 1; x.R1r = 1; x.T2f = 0.03; x.T2r = 1.3e-05; % Set simulation options Opt.SNR = 50; Opt.Method = 'Analytical equation'; Opt.ResetMz = false; % run simulation figure('Name','Single Voxel Curve Simulation'); FitResult = Model.Sim_Single_Voxel_Curve(x,Opt);

Warning: No MToff (i.e. no volumes acquired with Angles=0) -- Fitting assumes that MTData are already normalized. Warning: No MToff (i.e. no volumes acquired with Angles=0) -- Fitting assumes that MTData are already normalized.

## b- Sensitivity Analysis

|- Simulates sensitivity to fitted parameters: (1) vary fitting parameters from lower (lb) to upper (ub) bound. (2) run Sim_Single_Voxel_Curve Nofruns times (3) Compute mean and std across runs

% F kr R1f R1r T2f T2r OptTable.st = [0.16 30 1 1 0.03 1.3e-05]; % nominal values OptTable.fx = [0 1 1 1 1 1]; %vary F... OptTable.lb = [0.0001 0.0001 0.05 0.05 0.003 3e-06]; %...from 0.0001 OptTable.ub = [0.5 1e+02 5 5 0.5 5e-05]; %...to 0.5 % Set simulation options Opt.SNR = 50; Opt.Method = 'Analytical equation'; Opt.ResetMz = false; Opt.Nofrun = 5; % run simulation SimResults = Model.Sim_Sensitivity_Analysis(OptTable,Opt); figure('Name','Sensitivity Analysis'); SimVaryPlot(SimResults, 'F' ,'F' );

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