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"# I- DESCRIPTION"
]
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"qMRinfo('mwf'); % Describe the model"
]
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"source": [
"# II- MODEL PARAMETERS\n",
"## a- Create object"
]
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"Model = mwf;"
]
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"## Download sample data from OSF\n",
"> The current `Model` is an instance of `mwf` class.\n",
" \n",
"You can manually download the sample data for `mwf` [by clicking here](https:\/\/osf.io\/bes6f\/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",
"%          |- mwf object needs 1 protocol field(s) to be assigned:\n",
"%          |-   MET2data"
]
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{
"cell_type": "code",
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"EchoTime  = [10.0000; 20.0000; 30.0000; 40.0000; 50.0000; 60.0000; 70.0000; 80.0000; 90.0000; 100.0000; 110.0000; 120.0000; 130.0000; 140.0000; 150.0000; 160.0000; 170.0000; 180.0000; 190.0000; 200.0000; 210.0000; 220.0000; 230.0000; 240.0000; 250.0000; 260.0000; 270.0000; 280.0000; 290.0000; 300.0000; 310.0000; 320.0000];\n",
"% EchoTime (ms) is a vector of [32X1]\n",
"Model.Prot.MET2data.Mat = [ EchoTime ];\n",
"%%   "
]
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"source": [
"# III- FIT EXPERIMENTAL DATASET\n",
"## a- Load experimental data\n",
"%          |- mwf object needs 2 data input(s) to be assigned:\n",
"%          |-   MET2data\n",
"%          |-   Mask"
]
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" \n",
"% MET2data.mat contains [64  64   1  32] data.\n",
" load('mwf_data\/MET2data.mat');\n",
"% Mask.mat contains [64  64] data.\n",
" load('mwf_data\/Mask.mat');\n",
" data.MET2data= double(MET2data);\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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"cell_type": "markdown",
"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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"cell_type": "code",
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"collapsed": true
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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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{
"cell_type": "code",
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"      x = struct;\n",
"      x.MWF = 50.0001;\n",
"      x.T2MW = 20.0001;\n",
"      x.T2IEW = 120;\n",
"      % Set simulation options\n",
"      Opt.SNR = 200;\n",
"      Opt.T2Spectrumvariance_Myelin = 5;\n",
"      Opt.T2Spectrumvariance_IEIntraExtracellularWater = 20;\n",
"      % run simulation\n",
"      figure('Name','Single Voxel Curve Simulation');\n",
"      FitResult = Model.Sim_Single_Voxel_Curve(x,Opt);"
]
},
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"cell_type": "markdown",
"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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"      %              MWF           T2MW          T2IEW         \n",
"      OptTable.st = [50            20            1.2e+02]; % nominal values\n",
"      OptTable.fx = [0             1             1]; %vary MWF...\n",
"      OptTable.lb = [0.0001        0.0001        40]; %...from 0.0001\n",
"      OptTable.ub = [1e+02         40            2e+02]; %...to 100\n",
"      % Set simulation options\n",
"      Opt.SNR = 200;\n",
"      Opt.T2Spectrumvariance_Myelin = 5;\n",
"      Opt.T2Spectrumvariance_IEIntraExtracellularWater = 20;\n",
"      Opt.Nofrun = 5;\n",
"      % run simulation\n",
"      SimResults = Model.Sim_Sensitivity_Analysis(OptTable,Opt);\n",
"      figure('Name','Sensitivity Analysis');\n",
"      SimVaryPlot(SimResults, 'MWF' ,'MWF' );"
]
}
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