charmed_notebook.ipynb

{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# I- DESCRIPTION"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"qMRinfo('charmed'); % Describe the model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# II- MODEL PARAMETERS\n",
"## a- Create object"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Model = charmed;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download sample data from OSF\n",
"> The current `Model` is an instance of `charmed` class.\n",
" \n",
"You can manually download the sample data for `charmed` [by clicking here](https:\/\/osf.io\/u8n56\/download?version=3)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataDir = downloadData(Model,pwd);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## b- Set protocol\n",
"> Protocol is set according to the example data\n",
" \n",
"%          |- charmed object needs 1 protocol field(s) to be assigned:\n",
"%          |-   DiffusionData"
]
},
{
"cell_type": "code",
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"source": [
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"% Gnorm (T\/m) is a vector of [1791X1]\n",
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"% TE (s) is a vector of [1791X1]\n",
"Model.Prot.DiffusionData.Mat = [ Gx Gy Gz Gnorm  Delta  delta  TE ];\n",
"%%   "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# III- FIT EXPERIMENTAL DATASET\n",
"## a- Load experimental data\n",
"%          |- charmed object needs 3 data input(s) to be assigned:\n",
"%          |-   DiffusionData\n",
"%          |-   SigmaNoise\n",
"%          |-   Mask"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"% DiffusionData.nii.gz contains [64    64     1  1791] data.\n",
"data.DiffusionData=double(load_nii_data('charmed_data\/DiffusionData.nii.gz'));\n",
"% Mask.nii.gz contains [64  64] data.\n",
"data.Mask=double(load_nii_data('charmed_data\/Mask.nii.gz'));\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## b- Fit experimental data\n",
"> This section will fit data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"FitResults = FitData(data,Model,0);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"qMRshowOutput(FitResults,data,Model);"
]
},
{
"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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"FitResultsSave_nii(FitResults, 'charmed_data\/DiffusionData.nii.gz');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"      x = struct;\n",
"      x.fr = 0.5;\n",
"      x.Dh = 0.7;\n",
"      x.diameter_mean = 6;\n",
"      x.fcsf = 0;\n",
"      x.lc = 0;\n",
"      x.Dcsf = 3;\n",
"      x.Dintra = 1.4;\n",
"      % Set simulation options\n",
"      Opt.SNR = 50;\n",
"      % run simulation\n",
"      figure('Name','Single Voxel Curve Simulation');\n",
"      FitResult = Model.Sim_Single_Voxel_Curve(x,Opt);"
]
},
{
"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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"      %              fr            Dh            diameter_mean fcsf          lc            Dcsf          Dintra        \n",
"      OptTable.st = [0.5           0.7           6             0             0             3             1.4]; % nominal values\n",
"      OptTable.fx = [0             1             1             1             1             1             1]; %vary fr...\n",
"      OptTable.lb = [0             0.3           3             0             0             1             0.3]; %...from 0\n",
"      OptTable.ub = [1             3             10            1             8             4             3]; %...to 1\n",
"      % Set simulation options\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, 'fr' ,'fr' );"
]
}
],
"metadata": {
"kernelspec": {
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