filter_map: Applies spatial filtering (2D or 3D)ΒΆ

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Contents

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

I- DESCRIPTION

qMRinfo('filter_map'); % Describe the model
  filter_map:   Applies spatial filtering (2D or 3D)

Assumptions: If a 3D volume is provided and 2D filtering is requested, each slice will be processsed independently

Inputs:
Raw                Input data to be filtered
(Mask)             Binary mask to exclude voxels from smoothing

Outputs:
Filtered           Filtered output map (see FilterClass.m for more info)

Protocol:
NONE

Options:
(inherited from FilterClass)

Example of command line usage:

For more examples: a href="matlab: qMRusage(filter_map);"qMRusage(filter_map)/a

Author: Ilana Leppert Dec 2018

References:
Please cite the following if you use this module:
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 filter_map


II- MODEL PARAMETERS

a- create object

Model = filter_map;

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

         |- filter_map object needs 2 data input(s) to be assigned:
|-   Raw
|-   Mask
data = struct();
% Raw.nii.gz contains [128  128   35] data.
data.Raw=double(load_nii_data('filter_map_data/Raw.nii.gz'));
% Mask.nii.gz contains [128  128   35] data.
data.Mask=double(load_nii_data('filter_map_data/Mask.nii.gz'));

b- fit dataset

           |- This section will fit data.
FitResults = FitData(data,Model,0);
...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, 'filter_map_data/Raw.nii.gz');
Model.saveObj('filter_map_Demo.qmrlab.mat');
Warning: Directory already exists.

V- SIMULATIONS

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

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.