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"# I- DESCRIPTION"
]
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"source": [
"qMRinfo('inversion_recovery'); % Describe the model"
]
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"source": [
"# II- MODEL PARAMETERS\n",
"## a- Create object"
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"Model = inversion_recovery;"
]
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"## Download sample data from OSF\n",
"> The current `Model` is an instance of `inversion_recovery` class.\n",
" \n",
"You can manually download the sample data for `inversion_recovery` [by clicking here](https:\/\/osf.io\/cmg9z\/download?version=3)."
]
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"dataDir = downloadData(Model,pwd);"
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"## b- Set protocol\n",
"> Protocol is set according to the example data\n",
" \n",
"%          |- inversion_recovery object needs 2 protocol field(s) to be assigned:\n",
"%          |-   IRData\n",
"%          |-   TimingTable"
]
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"TI = [350.0000; 500.0000; 650.0000; 800.0000; 950.0000; 1100.0000; 1250.0000; 1400.0000; 1700.0000];\n",
"% TI(ms) is a vector of [9X1]\n",
"Model.Prot.IRData.Mat = [ TI];\n",
"%%   \n",
"TR = 2500;\n",
"Model.Prot.TimingTable.Mat = [ TR];\n",
"%%   "
]
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"source": [
"# III- FIT EXPERIMENTAL DATASET\n",
"## a- Load experimental data\n",
"%          |- inversion_recovery object needs 2 data input(s) to be assigned:\n",
"%          |-   IRData\n",
"%          |-   Mask"
]
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" \n",
"% IRData.mat contains [128  128    1    9] data.\n",
" load('inversion_recovery_data\/IRData.mat');\n",
"% Mask.mat contains [128  128] data.\n",
" load('inversion_recovery_data\/Mask.mat');\n",
" data.IRData= double(IRData);\n",
" data.Mask= double(Mask);"
]
},
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"source": [
"## b- Fit experimental data\n",
"> This section will fit data."
]
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"source": [
"FitResults = FitData(data,Model,0);"
]
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"source": [
"## c- Show fitting results\n",
"> * Output map will be displayed.\n",
" \n",
"> * If available, a graph will be displayed to show fitting in a voxel."
]
},
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"cell_type": "code",
"execution_count": null,
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"collapsed": true
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"source": [
"qMRshowOutput(FitResults,data,Model);"
]
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"metadata": {},
"source": [
"## d- Save results\n",
"> * qMR maps are saved in NIFTI and in a structure `FitResults.mat` that can be loaded in qMRLab graphical user interface.\n",
"> * Model object stores all the options and protocol\n",
"> * These objects can be easily shared or be used for simulation."
]
},
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"execution_count": null,
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"source": [
"FitResultsSave_nii(FitResults);"
]
},
{
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"source": [
"# IV- SIMULATIONS\n",
"> This section can be executed to run simulations for vfa_t1.\n",
" \n",
"## a- Single Voxel Curve\n",
"> Simulates single voxel curves:\n",
" \n",
"       1. Use equation to generate synthetic MRI data\n",
"       2. Add rician noise\n",
"       3. Fit and plot curve"
]
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"      x = struct;\n",
"      x.T1 = 600;\n",
"      x.rb = -1000;\n",
"      x.ra = 500;\n",
"      % Set simulation options\n",
"      Opt.SNR = 50;\n",
"      Opt.T1 = 600;\n",
"      Opt.M0 = 1000;\n",
"      Opt.TR = 3000;\n",
"      Opt.FAinv = 180;\n",
"      Opt.FAexcite = 90;\n",
"      Opt.Updateinputvariables = false;\n",
"      % run simulation\n",
"      figure('Name','Single Voxel Curve Simulation');\n",
"      FitResult = Model.Sim_Single_Voxel_Curve(x,Opt);"
]
},
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"metadata": {},
"source": [
"## b- Sensitivity analysis\n",
"> Simulates sensitivity to fitted parameters: \n",
" \n",
"       1. Vary fitting parameters from lower (lb) to upper (ub) bound.\n",
"       2. Run Sim_Single_Voxel_Curve Nofruns times\n",
"       3. Compute mean and std across runs"
]
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"      %              T1            rb            ra            \n",
"      OptTable.st = [6e+02         -1e+03        5e+02]; % nominal values\n",
"      OptTable.fx = [0             1             1]; %vary T1...\n",
"      OptTable.lb = [0.0001        -1e+04        0.0001]; %...from 0.0001\n",
"      OptTable.ub = [5e+03         0             1e+04]; %...to 5000\n",
"       Opt.SNR = 50;\n",
"       Opt.Nofrun = 5;\n",
"      % run simulation\n",
"      SimResults = Model.Sim_Sensitivity_Analysis(OptTable,Opt);\n",
"      figure('Name','Sensitivity Analysis');\n",
"      SimVaryPlot(SimResults, 'T1' ,'T1' );"
]
}
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