noise_level : Noise histogram fitting within a noise mask¶

1. Print noise_level information
2. Setting model parameters
2.a. Create noise_level object
2.b. Set protocol and options
2.b.1 Set protocol the CLI way
2.b.2 Set protocol and options the GUI way
3. Fit MRI data
3.a. Load input data
3.b. Execute fitting process
3.c. Display FitResults
3.d. Save fit results
3.e. Re-use or share fit configuration files
4. Simulations
4.a. Single Voxel Curve
4.b. Sensitivity Analysis
5. Notes
5.a. Notes specific to noise_level
5.b. Generic notes
5.b.1. Batch friendly option and protocol conventions
5.b.2 Parallelization:
6. Citations

% This m-file has been automatically generated using qMRgenBatch(noise_level)
% for publishing documentation.
% 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 noise_level on CLI.
%
% Demo files are downloaded into noise_level_data folder.
%
% Written by: Agah Karakuzu, 2017
% ==============================================================================

1. Print `noise_level` information

qMRinfo('noise_level');

  noise_level :  Noise histogram fitting within a noise mask
ASSUMPTIONS:
(1)Uniform noise distribution. Outputs are scalar : all voxels have
the same value

Fitted Parameters:
Non-central Chi Parameters
Sigma
eta
N

Options:
figure             plot noise histogram fit
Noise Distribution
Rician             valid if using one coil OR adaptive combine
Non-central Chi    valid for multi-coil and parallel imaging (parameter N reprensent the effective number of coils)

Author: Ian Gagnon, 2017

References:
Please cite the following if you use this module:
FILL
In addition to citing the package:
Karakuzu A., Boudreau M., Duval T.,Boshkovski T., Leppert I.R., Cabana J.F.,
Gagnon I., Beliveau P., Pike G.B., Cohen-Adad J., Stikov N. (2020), qMRLab:
Quantitative MRI analysis, under one umbrella doi: 10.21105/joss.02343

Reference page in Doc Center
doc noise_level

2. Setting model parameters

2.a. Create `noise_level` object

Model = noise_level;

2.b. Set `protocol` and `options`

Protocol: MRI acquisition parameters that are accounted for by the respective model.

For example: TE, TR, FA FieldStrength. The assigned protocol values are subjected to a sanity check to ensure that they are in agreement with the data attributes.

Options: Fitting preferences that are left at user's discretion.

For example: linear fit, exponential fit, drop first echo.

2.b.1 Set `protocol` the CLI way

If you are using Octave, or would like to serialize your operations any without GUI involvement, you can assign protocol directly in CLI:

See the generic notes section below for further information.

2.b.2 Set `protocol` and `options` the GUI way

The following command opens a panel to set protocol and options (if GUI is available to the user):

Model = Custom_OptionsGUI(Model);

If available, you need to close this panel for the remaining of the script to proceed.

Using this panel, you can save qMRLab protocol files that can be used in both interfaces. See the generic notes section below for details.

3. Fit MRI data

3.a. Load input data

This section shows how you can load data into a(n) noise_level object.

At the CLI level, qMRLab accepts structs containing (double) data in the fields named in accordance with a qMRLab model.

See the generic notes section below for BIDS compatible wrappers and scalable
qMRLab workflows.

%          |- noise_level object needs 2 data input(s) to be assigned:
%          |-   Data4D
%          |-   NoiseMask

data = struct();
% Data4D.nii.gz contains [70   70    4  197] data.
data.Data4D=double(load_nii_data('noise_level_data/Data4D.nii.gz'));
% NoiseMask.nii.gz contains [70  70   4] data.
data.NoiseMask=double(load_nii_data('noise_level_data/NoiseMask.nii.gz'));

3.b. Execute fitting process

This section will fit the loaded data.

FitResults = FitData(data,Model,0);

Visit the generic notes section below for instructions to accelerate fitting by
parallelization using ParFitData.

3.c. Display `FitResults`

You can display the current outputs by:

qMRshowOutput(FitResults,data,Model);

A representative fit curve will be plotted if available.

To render images in this page, we will load the fit results that had been saved before. You can skip the following code block;

% Load FitResults that comes with the example dataset.
FitResults_old = load('FitResults/FitResults.mat');
qMRshowOutput(FitResults_old,data,Model);

3.d. Save fit results

Outputs can be saved as *.nii.(gz) if NIfTI inputs are available:

% Generic function call to save nifti outputs
FitResultsSave_nii(FitResults, 'reference/nifti/file.nii.(gz)');

If not, FitResults.mat file can be saved. This file contains all the outputs as workspace variables:

% Generic function call to save FitResults.mat
FitResultsSave_mat(FitResults);

FitResults.mat files can be loaded to qMRLab GUI for visualization and ROI
analyses.

The section below will be dynamically generated in accordance with the example data format (mat or nii). You can substitute FitResults_old with FitResults if you executed the fitting using example dataset for this model in section 3.b..

FitResultsSave_nii(FitResults_old, 'noise_level_data/Data4D.nii.gz');

3.e. Re-use or share fit configuration files

qMRLab's fit configuration files (noise_level_Demo.qmrlab.mat) store all the options and protocol in relation to the used model and the release version.

*.qmrlab.mat files can be easily shared with collaborators to allow them fit their own
data or run simulations using identical option and protocol configurations.

Model.saveObj('my_noise_level_config.qmrlab.mat');

4. Simulations

4.a. `Single Voxel Curve`

Simulates single voxel curves

Analytically generate synthetic MRI data
Add rician noise
Fit and plot the respective curve

Not available for the current model.

4.b. `Sensitivity Analysis`

Simulates sensitivity to fitted parameters

Iterate fitting parameters from lower (lb) to upper (ub) bound
Run Sim_Single_Voxel_Curve for Nofruns times
Compute the mean and std across runs

Not available for the current model.

5. Notes

5.a. Notes specific to `noise_level`

Not provided.

5.b. Generic notes

5.b.1. Batch friendly `option` and `protocol` conventions

If you would like to load a desired set of options / protocols programatically, you can use *.qmrlab.mat files. To save a configuration from the protocol panel of noise_level, first open the respective panel by running the following command in your MATLAB command window (MATLAB only):

Custom_OptionsGUI(noise_level);

In this panel, you can arrange available options and protocols according to your needs, then click the save button to save my_noise_level.qmrlab.mat file. This file can be later loaded into a noise_level object in batch by:

Model = noise_level;
Model = Model.loadObj('my_noise_level.qmrlab.mat');

Model.loadObj('my_noise_level.qmrlab.mat') call won't update the fields in the Model object, unless the output is assigned to the object as shown above. This compromise on convenience is to retain Octave CLI compatibility.

If you don't have MATLAB, hence cannot access the GUI, two alternatives are available to populate options:

Use qmrlab/mcrgui:latest Docker image to access GUI. The instructions are available here.
Set options and protocols in CLI:

List available option fields using tab completion in Octave's command prompt (or window)

Model = noise_level;
Model.option. % click the tab button on your keyboard and list the available fields.

Assign the desired field. For example, for a mono_t2 object:

Model = mono_t2;
Model.options.DropFirstEcho = true;
Model.options.OffsetTerm = false;

Some option fields may be mutually exclusive or interdependent. Such cases are handled by the GUI options panel; however, not exposed to the CLI. Therefore, manual CLI options assignments may be challenging for some involved methods such as qmt_spgr or qsm_sb. If above options are not working for you and you cannot infer how to set options solely in batch, please feel free to open an issue in qMRLab and request the protocol file you need.

Similarly, in CLI, you can inspect and assign the protocols:

Model = noise_level;
Model.Prot. % click the tab button on your keyboard and list the available fields.

Each protocol field has two subfields of Format and Mat. The first one is a cell indicating the name of the protocol parameter (such as EchoTime (ms)) and the latter one contains the respective values (such as 30 x 1 double array containing EchoTimes).

The default Mat protocol values are set according to the example datasets served via OSF.

5.b.2 Parallelization:

The current model does not perform voxelwise fitting. Therefore, parallelization is not enabled.

6. Citations

qMRLab JOSS article

Karakuzu A., Boudreau M., Duval T.,Boshkovski T., Leppert I.R., Cabana J.F., Gagnon I., Beliveau P., Pike G.B., Cohen-Adad J., Stikov N. (2020), qMRLab: Quantitative MRI analysis, under one umbrella 10.21105/joss.02343

Reference article for noise_level

_Reference article is not defined for this model._

Quantitative MRI, under one umbrella.

| qMRPullseq | qMRFlow | Interactive Tutorials |

NeuroPoly Lab, Montreal, Canada

noise_level : Noise histogram fitting within a noise mask¶

Contents

1. Print noise_level information