qmt_bssfp_notebook.ipynb

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"# I- DESCRIPTION"
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"qMRinfo('qmt_bssfp'); % Describe the model"
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"# II- MODEL PARAMETERS\n",
"## a- Create object"
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"Model = qmt_bssfp;"
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"## Download sample data from OSF\n",
"> The current `Model` is an instance of `qmt_bssfp` class.\n",
" \n",
"You can manually download the sample data for `qmt_bssfp` [by clicking here](https:\/\/osf.io\/r64tk\/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",
"%          |- qmt_bssfp object needs 1 protocol field(s) to be assigned:\n",
"%          |-   MTdata"
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"Alpha = [5.0000; 10.0000; 15.0000; 20.0000; 25.0000; 30.0000; 35.0000; 40.0000; 35.0000; 35.0000; 35.0000; 35.0000; 35.0000; 35.0000; 35.0000; 35.0000];\n",
"% Alpha is a vector of [16X1]\n",
"Trf = [0.0003; 0.0003; 0.0003; 0.0003; 0.0003; 0.0003; 0.0003; 0.0003; 0.0002; 0.0003; 0.0004; 0.0006; 0.0008; 0.0012; 0.0016; 0.0021];\n",
"% Trf is a vector of [16X1]\n",
"Model.Prot.MTdata.Mat = [ Alpha Trf];\n",
"%%   "
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"# III- FIT EXPERIMENTAL DATASET\n",
"## a- Load experimental data\n",
"%          |- qmt_bssfp object needs 3 data input(s) to be assigned:\n",
"%          |-   MTdata\n",
"%          |-   R1map\n",
"%          |-   Mask"
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"% MTdata.nii.gz contains [128  128    1   16] data.\n",
"data.MTdata=double(load_nii_data('qmt_bssfp_data\/MTdata.nii.gz'));\n",
"% R1map.nii.gz contains [128  128] data.\n",
"data.R1map=double(load_nii_data('qmt_bssfp_data\/R1map.nii.gz'));\n",
"% Mask.nii.gz contains [128  128] data.\n",
"data.Mask=double(load_nii_data('qmt_bssfp_data\/Mask.nii.gz'));\n",
" "
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"## b- Fit experimental data\n",
"> This section will fit data."
]
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"FitResults = FitData(data,Model,0);"
]
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"## 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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"qMRshowOutput(FitResults,data,Model);"
]
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"## 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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"source": [
"FitResultsSave_nii(FitResults, 'qmt_bssfp_data\/MTdata.nii.gz');"
]
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"# 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.F = 0.1;\n",
"      x.kr = 30;\n",
"      x.R1f = 1;\n",
"      x.R1r = 1;\n",
"      x.T2f = 0.04;\n",
"      x.M0f = 1;\n",
"      % Set simulation options\n",
"      Opt.SNR = 50;\n",
"      Opt.Method = 'Analytical equation';\n",
"      Opt.ResetMz = false;\n",
"      % run simulation\n",
"      figure('Name','Single Voxel Curve Simulation');\n",
"      FitResult = Model.Sim_Single_Voxel_Curve(x,Opt);"
]
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"## 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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"      %              F             kr            R1f           R1r           T2f           M0f           \n",
"      OptTable.st = [0.1           30            1             1             0.04          1]; % nominal values\n",
"      OptTable.fx = [0             1             1             1             1             1]; %vary F...\n",
"      OptTable.lb = [0.0001        0.0001        0.2           0.2           0.01          0.0001]; %...from 0.0001\n",
"      OptTable.ub = [0.3           1e+02         3             3             0.2           2]; %...to 0.3\n",
"      % Set simulation options\n",
"      Opt.SNR = 50;\n",
"      Opt.Method = 'Analytical equation';\n",
"      Opt.ResetMz = false;\n",
"      Opt.Nofrun = 5;\n",
"      % run simulation\n",
"      SimResults = Model.Sim_Sensitivity_Analysis(OptTable,Opt);\n",
"      figure('Name','Sensitivity Analysis');\n",
"      SimVaryPlot(SimResults, 'F' ,'F' );"
]
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