noddi: Neurite Orientation Dispersion and Density ImagingĀ¶


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


qMRinfo('noddi'); % Describe the model
  noddi:   Neurite Orientation Dispersion and Density Imaging
Three-compartment model for fitting multi-shell DWI
a href="matlab: figure, imshow Diffusion.png ;"Pulse Sequence Diagram/a

Neuronal fibers model:
geometry                          sticks (Dperp = 0)
Orientation dispersion            YES (Watson distribution). Note that NODDI is more robust to
crossing fibers that DTI  (Campbell, NIMG 2017)

Permeability                      NO
Diffusion properties:
intra-axonal                      totally restricted
diffusion coefficient (Dr)      fixed by default.
extra-axonal                      Tortuosity model. Parallel diffusivity is equal to
intra-diffusivity.Perpendicular diffusivity is
proportional to fiber density
diffusion coefficient (Dh)      Constant

DiffusionData       4D diffusion weighted dataset
(Mask)               Binary mask to accelerate the fitting (OPTIONAL)

di                  Diffusion coefficient in the restricted compartment.
ficvf               Fraction of water in the restricted compartment.
fiso                Fraction of water in the isotropic compartment (e.g. CSF/Veins)
fr                  Fraction of restricted water in the entire voxel (e.g. intra-cellular volume fraction)
fr = ficvf*(1-fiso)
diso (fixed)        diffusion coefficient of the isotropic compartment (CSF)
kappa               Orientation dispersion index
b0                  Signal at b=0
theta               angle of the fibers
phi                 angle of the fibers

Multi-shell diffusion-weighted acquisition
at least 2 non-zeros bvalues
at least 5 b=0 (used to compute noise standard deviation

DiffusionData       Array [NbVol x 7]
Gx                Diffusion Gradient x
Gy                Diffusion Gradient y
Gz                Diffusion Gradient z
Gnorm (T/m)         Diffusion gradient magnitude
Delta (s)         Diffusion separation
delta (s)         Diffusion duration
TE (s)            Echo time

Model               Model part of NODDI.
Available models are:
-WatsonSHStickTortIsoVIsoDot_B0 is a four model compartment used for ex-vivo datasets

Example of command line usage
For more examples: a href="matlab: qMRusage(noddi);"qMRusage(noddi)/a

Author: Tanguy Duval

Please cite the following if you use this module:
Zhang, H., Schneider, T., Wheeler-Kingshott, C.A., Alexander, D.C., 2012. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61, 1000?1016.
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 noddi


a- create object

Model = noddi;

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.


a- load experimental data

         |- noddi object needs 2 data input(s) to be assigned:
|-   DiffusionData
|-   Mask
data = struct();
% DiffusionData.nii.gz contains [74   87   50  109] data.
% Mask.nii.gz contains [74  87  50] data.

b- fit dataset

           |- This section will fit data.
FitResults = FitData(data,Model,0);
=============== qMRLab::Fit ======================
Operation has been started: noddi
Elapsed time is 1.845969 seconds.
Operation has been completed: noddi

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');

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, 'noddi_data/DiffusionData.nii.gz');
Warning: Directory already exists.


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

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
      x = struct;
x.ficvf = 0.5;
x.di = 1.7;
x.kappa = 0.05;
x.fiso = 0;
x.diso = 3;
x.b0 = 1;
x.theta = 0.2;
x.phi = 0;
Opt.SNR = 50;
% run simulation
figure('Name','Single Voxel Curve Simulation');
FitResult = Model.Sim_Single_Voxel_Curve(x,Opt);

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
      %              ficvf         di            kappa         fiso          diso          b0            theta         phi = [0.5           1.7           0.05          0             3             1             0.2           0]; % nominal values
OptTable.fx = [0             1             1             1             1             1             1             1]; %vary ficvf... = [0             1.3           0.05          0             1             0             0             0]; %...from 0
OptTable.ub = [1             2.1           0.8           1             5             1e+03         3.1           3.1]; 1
Opt.SNR = 50;
Opt.Nofrun = 5;
% run simulation
SimResults = Model.Sim_Sensitivity_Analysis(OptTable,Opt);
figure('Name','Sensitivity Analysis');
SimVaryPlot(SimResults, 'ficvf' ,'ficvf' );