## page was renamed from brainsuiteBDP
## page was renamed from brainsuite
= FEM tensors estimation with BrainSuite =
''Authors: [[https://neuroimage.usc.edu/brainstorm/AboutUs/tmedani|Takfarinas Medani]], Francois Tadel, Anand Joshi and Richard Leahy''

'''[TUTORIAL UNDER WRITING: NOT READY FOR PUBLIC USE]'''

In this tutorial, we describe the process of the estimation of the realistic conductivity tensors for the brain tissues using the [[http://brainsuite.org/|BrainSuite software]].  The main purpose is to generate the conductivity tensor for the FEM computation as introduced on [[https://neuroimage.usc.edu/brainstorm/Tutorials/Duneuro|this page]].

The realistic tensors are estimated from the Diffusion-Weighted Images (DWI). For this purpose, Brainstorm calls internally the BrainSuite software to compute the diffusion tensors on each brain voxel. Afterward, the Effective Medium Approach (EMA) is applied to convert the diffusion tensors to the conductivity tensors.

The following section shows the users how to do it from the Brainstorm graphical interface.

Further documentation about previous usage of Brainsuite within Brainstorm can be found in this [[https://neuroimage.usc.edu/brainstorm/Tutorials/SegBrainSuite|page]].

<<TableOfContents(2,2)>>

== Requirement ==
 * You have already followed all the introduction tutorials
 * You have a working copy of Brainstorm installed on your computer
 * For the DWI data, only the NIfTi files are supported

== Brainsuite Installation ==
 1. Download the latest version of BrainSuite from http://www.brainsuite.org/download.

 1. Install it on your computer by following the instructions in [[http://brainsuite.bmap.ucla.edu/quickstart/installation/|BrainSuite's quick start installation guide]].

 1. Note that you will be using BrainSuite Diffusion Pipeline(BDP), so you need to install a compatible [[http://www.mathworks.com/products/compiler/mcr|MATLAB Compiler Runtime]](last version).

 1. Start BrainSuite to check if the installation (It's not required to open BrainSuite to run this tutorial).

 1. The BrainSuite installation folder should be informed in the Brainstorm preferences

{{attachment:brainsuiteInstalation.JPG||height="250",width="650"}}

----
== Dataset ==
In this tutorial, we use the Brainsuite dataset example available on the BrainSuite  [[http://brainsuite.org/tutorials/|tutorial webpage]]. User can also download directly these data from these links: [[http://brainsuite.org/WebTutorialData/BrainSuiteTutorialSVReg_Sept16.zip|MRI T1w]] and [[http://brainsuite.org/WebTutorialData/DWI_Feb15.zip|MRI DWI]]

The first file contains the T1 MRI data, with the name '2523412.nii.gz'. as in this figure,

{{attachment:vierMriFolder.JPG||height="130",width="580"}}

The second file is the DWI and should contain at least three files

{{attachment:vierDWIFolder.JPG||height="150",width="580"}}

Where the *.bval is a text file that contains the value of the gradient, and the *.bvec is also a text file that contains the orientation of the gradient. The nii.gz file is the NifTi file of the DWI where the images are stored.

== Realistic condctivity tensors ==
=== Load the T1 MRI data to brainstorm ===
First, you need to create a new subject in your protocol, let call it the 'BrainSuiteSubject'. Then import the T1 MRI of the subject and set the fiducials points as explained in the [[https://neuroimage.usc.edu/brainstorm/Tutorials/ImportAnatomy|previous tutorial]].

The T1 MRI is required since all the BDP uses the T1 space for its computation. Furthermore, this is required since it will be used to align the tensors to the FEM mesh later.

{{attachment:mri.JPG||height="100",width="250"}}

=== Diffusion tensor generation (DTI) from DWI ===
In this step, Brainstorm calls the Brainsuite internally, and the diffusion tensors are computed.

Right-click on the subject and then select the item "Convert DWI to DTI".

{{attachment:dwi2dti.jpg||height="300",width="550"}}

Then follow the popup windows by selecting the DWI, you may need to extract the zip file before.

{{attachment:importDWI.JPG||height="280",width="650"}}

If the bval, and bval files are in the same folder, Brainstorm will detect them automatically, otherwise, the user will be asked to browse the files one by one (as it's the case in this tutorial).

Brainstorm calls internally the BrainSuite process, and compute the diffusion tensors.

At the end of this process, a new node will appear in the Brainstorm database with the name 'DTI-EIT'. This name refers to, DTI: diffusion tensors images, and EIG for eigenvalue, since the eigenvalues and eigenvectors are computed at voxel and stored in Brainstorm database.

{{attachment:mriAndDTI-EIG.JPG||height="100",width="230"}}

If you check the structure of the file DTI-EIG, by right click -> File and then 'Display file contents', the following figure is displayed.

{{attachment:EIG-hardDisc.JPG||height="200",width="450"}}

The size of the matrix is 128x256x256x12, where the first 3 values are the same as the size of the T1 MRI and 12 corresponds to the 3 eigenvectors components (9) and eigenvalues (3)

=== Conductivity tensor generation from DTI ===
The Effective Medium Approach is applied to convert the diffusion tensors to the conductivity tensors.

==== FEM mesh head model ====
This step requires the FEM mesh of the head model. You can generate the FEM head model from the MRI data as explained on [[https://neuroimage.usc.edu/brainstorm/Tutorials/FemMesh|this page]].

For the following, we used the SimNibs FEM mesh generation. The following figure shows the FEM mesh obtained with the SimNibs method using the T1 MRI.

{{attachment:Mri&femMeshView.JPG|Mri&femMeshView.JPG|height="300",width="260"}} {{attachment:femMeshView.JPG||height="300",width="280"}}

Note that this mesh is obtained only from the T1, the use of the T2 is highly recommended if it's available, as recommended in the [[https://neuroimage.usc.edu/brainstorm/Tutorials/FemMesh|FEM mesh tutorial]].

==== Computation of FEM mesh tensors ====
Once the FEM mesh and the DTI tensors are available in the Brainstorm database, the next step for the FEM tensors can be performed by the following:

- Right-click on the FEM mesh - Compute FEM tensors

{{attachment:menuGenerateFemTensors.png||height="280",width="250"}}

Brainstorm checks the available tissues in the FEM head model and displays the following panel

{{attachment:FEMConductivitiesIsoPanel.JPG||height="220",width="250"}}

This panel lists the tissues available in the FEM head model and assigns a default value of the conductivity for each compartment. Users can change these values to their own if needed.

DTI values can be used to generate conductivity tensors for the white matter (and in some cases for the grey matter). Please, note that the DWI can be used only for the brain tissues and not for the outers compartments (skull and skin)

In this tutorial (and in most cases) we select the white matter. Select the WM anisotropy and keep all the other tissues as isotropic, then these additional options appear asking for the method to use.

{{attachment:FEMConductivitiesAnisoPanel.JPG||height="300",width="250"}}

The available methods are:

- Effective Medium approach (EMA)

- Effective Medium approach with volume constraints  (EMA + VC)

- Simulated or the artificial anisotropy

Only the two first methods require the DTI. More information about these methods can be found on these references [ref1][ref2] and in our main paper [link]

In this tutorial, we use the method "EMA + VC", where the final tensors are constrained to fits the volume of the equivalent isotropic tensor volume.

==== Visulation of FEM mesh tensors ====
Once the FEM tensors are successfully computed, they are stored in the FEM head node. By right-clicking on the FEM head, new menu items are added that gives the possibilities to display the FEM tensors either as ellipsoids or as vectors in the direction of the main eigenvector.

{{attachment:menuDisplayTensors.jpg||height="300",width="250"}}

The tensors can be displayed either on the FEM mesh or overlaid on the MRI. The following figures show an example of the obtained tensors displayed on the white matter.

{{attachment:meshViewTensorsLines.JPG||height="300",width="350"}} {{attachment:meshViewTensorsTensorsTops.JPG||height="300",width="350"}}

On the left, the tensors as a line on the direction of the main eigenvector. On the right, the tensors displayed as ellipsoids. The orientation of the tensor is color-coded as follows: red for right-left, green for anterior-posterior, and blue for superior-inferior.

Note that the quality of the tensors depends on the DWI data and the number of acquisition direction.

Users can also display the tensors on specific tissues, for example on the white matter (left figure) or overlay on the MRI (right figure).

{{attachment:meshViewTensorsLinesWM2.JPG||height="300",width="350"}} {{attachment:tensorsOnMri.JPG||height="300",width="300"}}

==== Recommendation ====
In the case where the user wants to use generate isotropic tensors, then the DTI is not required. In the case where more than one FEM head model is in the database, the highlighted one in the green color will be used.

<<TAG(Advanced)>>

== Artificial/simulated conductivity tensors ==
In the case where the DWI is not available, or in the case where the users desire to evaluate the effect of the conductivity change on the head model, the artificial conductivity can be used.

Users can reach this option by following this tutorial and select the third method in this panel. {{attachment:artificialTensors.JPG||height="450",width="280"}}

Two approaches are integrated within Brainstorm. Either Wang's constraint or the volume's constraint (Wolters). The common feature between these methods is the ratio between the transversal and longitudinal conductivity ratio.

A common example is the skull anisotropy simulation, where the longitudinal conductivity can be higher than the transversal conductivity, the ratio can vary from 2 to 10 [ref]. In this tutorial, we keep all the tissue as isotropic, except the skull, we use a ratio of 0.1 and select the volume constraint. The following figures show the results of this example.
{{attachment:skullAniso.JPG||height="280",width="280"}} {{attachment:skullAniso2.JPG||height="280",width="280"}}

== References ==