inversion_recovery: Compute a T1 map using Inversion Recovery dataΒΆ

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Contents

% This m-file has been automatically generated using qMRgenBatch(inversion_recovery)
% 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 inversion_recovery on CLI.
%
% Demo files are downloaded into inversion_recovery_data folder.
%
% Written by: Agah Karakuzu, 2017
% =========================================================================

I- DESCRIPTION

qMRinfo('inversion_recovery'); % Describe the model
 inversion_recovery: Compute a T1 map using Inversion Recovery data

Assumptions:
(1) Gold standard for T1 mapping
(2) Infinite TR

Inputs:
IRData      Inversion Recovery data (4D)
(Mask)      Binary mask to accelerate the fitting (OPTIONAL)

Outputs:
T1          transverse relaxation time [ms]
b           arbitrary fit parameter (S=a + b*exp(-TI/T1))
a           arbitrary fit parameter (S=a + b*exp(-TI/T1))
idx         index of last polarity restored datapoint (only used for magnitude data)
res         Fitting residual


Protocol:
IRData  [TI1 TI2...TIn] inversion times [ms]

Options:
Method          Method to use in order to fit the data, based on whether complex or only magnitude data acquired.
'complex'         RD-NLS (Reduced-Dimension Non-Linear Least Squares)
S=a + b*exp(-TI/T1)
'magnitude'      RD-NLS-PR (Reduced-Dimension Non-Linear Least Squares with Polarity Restoration)
S=|a + b*exp(-TI/T1)|

Example of command line usage (see also a href="matlab: showdemo inversion_recovery_batch"showdemo inversion_recovery_batch/a):
Model = inversion_recovery;  % Create class from model
Model.Prot.IRData.Mat=[350.0000; 500.0000; 650.0000; 800.0000; 950.0000; 1100.0000; 1250.0000; 1400.0000; 1700.0000];
data = struct;  % Create data structure
data.MET2data ='IRData.mat';  % Load data
data.Mask = 'Mask.mat';
FitResults = FitData(data,Model); %fit data
FitResultsSave_mat(FitResults);

For more examples: a href="matlab: qMRusage(minversion_recovery);"qMRusage(inversion_recovery)/a

Author: Ilana Leppert, 2017

References:
Please cite the following if you use this module:
A robust methodology for in vivo T1 mapping. Barral JK, Gudmundson E, Stikov N, Etezadi-Amoli M, Stoica P, Nishimura DG. Magn Reson Med. 2010 Oct;64(4):1057-67. doi: 10.1002/mrm.22497.
In addition to citing the package:
Cabana J-F, Gu Y, Boudreau M, Levesque IR, Atchia Y, Sled JG, Narayanan S, Arnold DL, Pike GB, Cohen-Adad J, Duval T, Vuong M-T and Stikov N. (2016), Quantitative magnetization transfer imaging made easy with qMTLab: Software for data simulation, analysis, and visualization. Concepts Magn. Reson.. doi: 10.1002/cmr.a.21357


Reference page in Doc Center
doc inversion_recovery


II- MODEL PARAMETERS

a- create object

Model = inversion_recovery;

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

         |- inversion_recovery object needs 2 data input(s) to be assigned:
|-   IRData
|-   Mask
data = struct();

% IRData.mat contains [128  128    1    9] data.
load('inversion_recovery_data/IRData.mat');
% Mask.mat contains [128  128] data.
load('inversion_recovery_data/Mask.mat');
data.IRData= double(IRData);
data.Mask= double(Mask);

b- fit dataset

           |- This section will fit data.
FitResults = FitData(data,Model,0);
Starting to fit data.

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);

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('inversion_recovery_Demo.qmrlab.mat');
Warning: Directory already exists.

V- SIMULATIONS

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

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.T1 = 600;
x.rb = -1000;
x.ra = 500;
% Set simulation options
Opt.SNR = 50;
Opt.T1 = 600;
Opt.M0 = 1000;
Opt.TR = 3000;
Opt.FAinv = 180;
Opt.FAexcite = 90;
Opt.Updateinputvariables = false;
% run simulation
figure('Name','Single Voxel Curve Simulation');
FitResult = Model.Sim_Single_Voxel_Curve(x,Opt);

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
      %              T1            rb            ra
OptTable.st = [6e+02         -1e+03        5e+02]; % nominal values
OptTable.fx = [0             1             1]; %vary T1...
OptTable.lb = [0.0001        -1e+04        0.0001]; %...from 0.0001
OptTable.ub = [5e+03         0             1e+04]; %...to 5000
Opt.SNR = 50;
Opt.Nofrun = 5;
% run simulation
SimResults = Model.Sim_Sensitivity_Analysis(OptTable,Opt);
figure('Name','Sensitivity Analysis');
SimVaryPlot(SimResults, 'T1' ,'T1' );