mtv : Macromolecular Tissue Volume in brain¶


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

1. Print mtv information

  mtv :  Macromolecular Tissue Volume in brain

(1) Organ: brain (MTV needs to be adapted for other organs)
(2) Reception profile (B1-) is estimated through a spline approximation.
Contrary to the original paper (see citation), multi-coil data are
not necessary. Although simpler, more validation is needed.
For multi-coil datasets, please use mrQ (

T1           T1 map from Spoiled Gradient Echo data (use vfa_t1 module)
M0           M0 from Spoiled Gradient Echo data (use vfa_t1 module)
Mask         Mask of the entire brain (REQUIRED). This mask is eroded for
border effects and clustured into white matter (WM) and
CerebroSpinal Fluid Mask (CSF).
In the WM mask, coil sensitivity is computed assuming:
M0 = g * PD = g * 1 / (A + B/T1) with A~0.916 & B~0.436
The CSF mask is used for proton density normalization
(assuming ProtonDensity_CSF = 1)

MTV                 Macromolecular Tissue Volume (normal values in the brain range [0 0.4])
CoilGain            Reception profile of the antenna (B1- map).
relative (pixel-wise) normalization of M0
CSF                 CSF mask (cleaned).
absolute (global) normalization of M0
seg                 Clustering of the eroded input mask in four categories:
1: Gray Matter
2: Deep Gray
3: White matter (used for Coil Gain)
4: CSF


Voxel Size          [1x3] Size of the voxels (in mm)
Spline Smoothness   Smoothness parameter for the coil gain. The larger S
is, the smoother the coil gain map will be.
CSF T1 threshold    Threshold on T1 for the CSF mask (in s).

Example of command line usage:
For more examples: qMRusage(mtv)

Author: Tanguy Duval, 2020

Please cite the following if you use this module:
Mezer A, Yeatman JD, Stikov N, Kay KN, Cho NJ, Dougherty RF, Perry ML, Parvizi J, Hua le H, Butts-Pauly K, Wandell BA. Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging. Nat Med. 2013
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 mtv

2. Setting model parameters

2.a. Create mtv object

Model = mtv;

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) mtv 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.

%          |- mtv object needs 3 data input(s) to be assigned:
%          |-   T1
%          |-   M0
%          |-   Mask

data = struct();
% T1.nii.gz contains [192  256  160] data.
% M0.nii.gz contains [192  256  160] data.
% Mask.nii.gz contains [192  256  160] data.

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:


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');

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

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

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, 'mtv_data/T1.nii.gz');

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

qMRLab's fit configuration files (mtv_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.


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 mtv

5.a.1 BIDS

|== sub-01/
|~~~~~~ anat/
|---------- sub-01_flip-1_VFA.json
|---------- sub-01_flip-1_VFA.nii.gz
|---------- sub-01_flip-2_VFA.json
|---------- sub-01_flip-2_VFA.nii.gz
|---------- .
|---------- .
|---------- sub-01_flip-N_VFA.json
|---------- sub-01_flip-N_VFA.nii.gz
|== derivatives/
|~~~~~~ qMRLab/
|---------- dataset_description.json
|~~~~~~~~~~ sub-01/anat/
|-------------- sub-01_T1map.nii.gz
|-------------- sub-01_T1map.json
|-------------- sub-01_M0map.nii.gz
|-------------- sub-01_M0map.json
|-------------- sub-01_MTVmap.nii.gz (Not defined in BIDS yet)
|-------------- sub-01_MTVmap.json
|~~~~~~ pipeline/
|---------- dataset_description.json
|~~~~~~~~~~ sub-01/anat/
|-------------- sub-01_label-brain_mask.nii.gz
|-------------- sub-01_label-brain_mask.json

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


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

Model = mtv;
Model = Model.loadObj('my_mtv.qmrlab.mat');

  Model.loadObj('my_mtv.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 = mtv;
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 = mtv;
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 mtv

Mezer A, Yeatman JD, Stikov N, Kay KN, Cho NJ, Dougherty RF, Perry ML, Parvizi J, Hua le H, Butts-Pauly K, Wandell BA. Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging. Nat Med. 2013

Quantitative MRI, under one umbrella.

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