denoising_mppca : 4d image denoising and noise map estimation

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Contents

% This m-file has been automatically generated using qMRgenBatch(denoising_mppca)
% for publishing documentation.
% Command Line Interface (CLI) is well-suited for automatization
% purposes and Octave.
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% Please execute this m-file section by section to get familiar with batch
% processing for denoising_mppca on CLI.
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% Demo files are downloaded into denoising_mppca_data folder.
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% Written by: Agah Karakuzu, 2017
% ==============================================================================

1. Print denoising_mppca information

qMRinfo('denoising_mppca');

  denoising_mppca :  4d image denoising and noise map estimation
by exploiting data redundancy in the PCA domain using universal
properties of the eigenspectrum or random covariance matrices,
i.e. Marchenko Pastur distribution

Assumptions:
Noise follows a rician distribution
image bounderies are not processed

Inputs:
Data4D              4D data (any modality)
(Mask)               Binary mask with region-of-interest. (OPTIONAL)

Outputs:
Data4D_denoised     denoised 4D data
sigma_g               standard deviation of the rician noise

Options:
sampling
'full'          sliding window
'fast'          block processing (warning: undersampled noise map will be returned)
kernel              window size, typically in order of [5 x 5 x 5]

Example of command line usage:
Model = denoising_mppca;  % Create class from model
data.Data4D = load_nii_data('Data4D.nii.gz');  % Load data
FitResults = FitData(data,Model,1);  % Fit each voxel within mask
FitResultsSave_nii(FitResults,'Data4D.nii.gz');  % Save in local folder: FitResults/

Author: Tanguy Duval, 2016

References:
Please cite the following if you use this module:
Veraart, J.; Fieremans, E. & Novikov, D.S. Diffusion MRI noise mapping using random matrix theory Magn. Res. Med., 2016, early view, doi:10.1002/mrm.26059
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 denoising_mppca

2. Setting model parameters

2.a. Create denoising_mppca object

Model = denoising_mppca;

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) denoising_mppca 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. 

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

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

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

qMRLab's fit configuration files (denoising_mppca_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_denoising_mppca_config.qmrlab.mat');

4. Simulations

4.a. Single Voxel Curve

Simulates single voxel curves
  1. Analytically generate synthetic MRI data
  2. Add rician noise
  3. Fit and plot the respective curve

Not available for the current model.

4.b. Sensitivity Analysis

Simulates sensitivity to fitted parameters
  1. Iterate fitting parameters from lower (lb) to upper (ub) bound
  2. Run Sim_Single_Voxel_Curve for Nofruns times
  3. Compute the mean and std across runs

Not available for the current model.

5. Notes

5.a. Notes specific to denoising_mppca

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 denoising_mppca, first open the respective panel by running the following command in your MATLAB command window (MATLAB only):

Custom_OptionsGUI(denoising_mppca);

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

Model = denoising_mppca;
Model = Model.loadObj('my_denoising_mppca.qmrlab.mat');

  Model.loadObj('my_denoising_mppca.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:

  1. Use qmrlab/mcrgui:latest Docker image to access GUI. The instructions are available here.
  2. Set options and protocols in CLI:
  • List available option fields using tab completion in Octave's command prompt (or window)
Model = denoising_mppca;
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 = denoising_mppca;
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 denoising_mppca

_Reference article is not defined for this model._

Quantitative MRI, under one umbrella.

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NeuroPoly Lab, Montreal, Canada


Published with MATLAB® R2018b