noise_level : Noise histogram fitting within a noise maskΒΆ

https://mybinder.org/badge_logo.svg

Contents

% This m-file has been automatically generated using qMRgenBatch(noise_level)
% 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
% =========================================================================

I- DESCRIPTION

qMRinfo('noise_level'); % Describe the model
  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:
Cabana J-F, Gu Y, Boudreau M, Levesque IR, Atchia Y, Sled JG, Narayanan S, Arnold DL, Pike GB, Cohen-Adad J, Duval T, Vuong M-T and Stikov N. (2016), Quantitative magnetization transfer imaging made easy with qMTLab: Software for data simulation, analysis, and visualization. Concepts Magn. Reson.. doi: 10.1002/cmr.a.21357

Reference page in Doc Center
doc noise_level


II- MODEL PARAMETERS

a- create object

Model = noise_level;

b- modify options

         |- This section will pop-up the options GUI. Close window to continue.
|- Octave is not GUI compatible. Modify Model.options directly.
Model = Custom_OptionsGUI(Model); % You need to close GUI to move on.

III- FIT EXPERIMENTAL DATASET

a- load experimental data

         |- 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'));

b- fit dataset

           |- This section will fit data.
FitResults = FitData(data,Model,0);
     N        eta      sigma_g
1.0000    0.0000    7.8621

...done

c- show fitting results

         |- Output map will be displayed.
|- If available, a graph will be displayed to show fitting in a voxel.
|- To make documentation generation and our CI tests faster for this model,
we used a subportion of the data (40X40X40) in our testing environment.
|- Therefore, this example will use FitResults that comes with OSF data for display purposes.
|- Users will get the whole dataset (384X336X224) and the script that uses it for demo
via qMRgenBatch(qsm_sb) command.
FitResults_old = load('FitResults/FitResults.mat');
qMRshowOutput(FitResults_old,data,Model);

d- Save results

         |-  qMR maps are saved in NIFTI and in a structure FitResults.mat
that can be loaded in qMRLab graphical user interface
|-  Model object stores all the options and protocol.
It can be easily shared with collaborators to fit their
own data or can be used for simulation.
FitResultsSave_nii(FitResults, 'noise_level_data/Data4D.nii.gz');
Model.saveObj('noise_level_Demo.qmrlab.mat');
Warning: Directory already exists.

V- SIMULATIONS

   |- This section can be executed to run simulations for noise_level.

a- Single Voxel Curve

         |- Simulates Single Voxel curves:
(1) use equation to generate synthetic MRI data
(2) add rician noise
(3) fit and plot curve
% Not available for the current model.

b- Sensitivity Analysis

         |-    Simulates sensitivity to fitted parameters:
(1) vary fitting parameters from lower (lb) to upper (ub) bound.
(2) run Sim_Single_Voxel_Curve Nofruns times
(3) Compute mean and std across runs
% Not available for the current model.