inversion_recovery: Compute a T1 map using Inversion Recovery dataΒΆ


% 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
% =========================================================================


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

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

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

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

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

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

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

Author: Ilana Leppert, 2017

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


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.


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.
% Mask.mat contains [128  128] data.
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');

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.
Warning: Directory already exists.


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