amico: Accelerated Microstructure Imaging via Convex OptimizationĀ¶


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


qMRinfo('amico'); % Describe the model
  amico:   Accelerated Microstructure Imaging via Convex Optimization
Sub-module of noddi
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)
irfrac              Fraction of isotropically restricted compartment (Dot for ex vivo model)
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:
Alessandro Daducci, Erick Canales-Rodriguez, Hui Zhang, Tim Dyrby, Daniel Alexander, Jean-Philippe Thiran, 2015. Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data. NeuroImage 105, pp. 32-44
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 amico


a- create object

Model = amico;

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

         |- amico 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);
- Precomputing rotation matrices for l_max=12:
[ already computed ]

- Generating kernels with model "NODDI" for protocol "example":
[ Kernels already computed. Set "doRegenerate=true" to force regeneration ]

- Resampling rotated kernels:
[========                 ] 
Error using load
Unable to read file '/Users/Agah/Desktop/neuropoly/qMRLab/External/AMICO/AMICO_matlab/exports/example/kernels/NODDI/A_054.mat'. No such file or directory.

Error in AMICO_NODDI/ResampleKernels (line 118)
load( fullfile( ATOMS_path, sprintf('A_%03d.mat',progress.i) ), 'lm' );

Error in AMICO_ResampleKernels (line 48)
CONFIG.model.ResampleKernels( fullfile(AMICO_data_path,CONFIG.protocol,'kernels',, idx_OUT, Ylm_OUT );

Error in amico/Precompute (line 140)

Error in FitData (line 49)
if ismethod(Model,'Precompute'), Model = Model.Precompute; end

Error in amico_batch (line 44)
FitResults = FitData(data,Model,0);

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, 'amico_data/DiffusionData.nii.gz');


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

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' );