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

In this tutorial, we describe the estimation of realistic conductivity tensors of living brain tissues using the [[http://brainsuite.org/|BrainSuite software]].   These results are used in FEM forward modeling, as described in the tutorials: [[https://neuroimage.usc.edu/brainstorm/Tutorials/Duneuro#DUNEuro_options:_Advanced|FEM with DUNEuro]] and [[https://neuroimage.usc.edu/brainstorm/Tutorials/FemMedianNerveCharm#FEM_tensors|FEM median nerve example]].

The realistic tensors are estimated from the Diffusion-Weighted Images (DWI): Brainstorm calls the BrainSuite software to compute the diffusion tensors on each brain MRI voxel (DTI), then Effective Medium Approach (EMA) is applied to estimate the conductivity tensors for each element of a tetrahedral FEM mesh. This is particularly interesting for the modeling the anisotropy of the white  matter.

BrainSuite is also used for other purposes in Brainstorm, particularly the T1 MRI segmentation, as  documented in this tutorial: [[Tutorials/SegBrainSuite|MRI segmentation: BrainSuite]].

<<TableOfContents(2,2)>>

== Download and installation ==
==== Requirements ====
 * 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 (.nii) are supported.

==== Install Brainsuite ====
 1. Download the latest version of BrainSuite from http://forums.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. You will be using BrainSuite Diffusion Pipeline (BDP), so you need to install a compatible [[https://www.mathworks.com/products/compiler/matlab-runtime.html|MATLAB Runtime]] (2019b for BrainSuite 21a).

 1. In Brainstorm, menu File > Edit preferences > Enter the BrainSuite installation folder:<<BR>><<BR>> {{attachment:brainsuiteInstall.gif}}

==== Download the dataset ====
 * Download the files: [[http://brainsuite.org/WebTutorialData/BrainSuiteTutorialSVReg_Sept16.zip|MRI T1w]] and [[http://brainsuite.org/WebTutorialData/DWI_Feb15.zip|MRI DWI]] (from the [[http://brainsuite.org/tutorials/dtiexercise/|BrainSuite diffusion tutorial]]).

 * Unzip them outside of any of the Brainstorm folders (program   folder or database folder).
 * Start Brainstorm (Matlab scripts or stand-alone version)
 * Select the menu File > Create new protocol. Name it "'''TutorialTensors'''" and select:
  * No, use individual anatomy
  * No, use one channel file per condition

== Import the anatomy ==
=== T1 MRI ===
 * Switch to the "anatomical data" view, the left button in the toolbar above the database explorer.
 * Right-click on the TutorialFem folder > New subject > '''Subject01'''
  * Keep the default options you set for the protocol.

 * Right-click on the subject node > '''Import MRI''':
  * Set the file format: '''All MRI files (subject space)'''

  * Select the T1 file: BrainSuiteTutorialSVReg/'''2523412.nii.gz'''

 * Click on the link "'''Click here to compute MNI normalization'''": option "'''maff8'''". This estimates an affine transformation to the [[https://neuroimage.usc.edu/brainstorm/CoordinateSystems#MNI_coordinates|MNI space]] and sets default positions for the anatomical fiducials. The NAS/LPA/RPA fiducials are needed for defining the Brainstorm [[CoordinateSystems|subject coordinate system]], in which the surfaces and FEM meshes are stored. <<BR>><<BR>> {{attachment:importT1.gif}}

=== Diffusion imaging ===
This computes the  This requires BrainSuite to be installed on your computer, with the bdp program available in the system path.

 * Right-click on Subject01''' '''> '''Convert DWI to DTI'''

 * Select the DWI file: DWI/'''2523412.dwi.nii.gz'''

 * The associated text files '''*.bvec''' (orientation of the gradient) and '''*.bval''' (value of the gradient) must be in the same folder, with the same file name. Theses files are created from for the DWI acquisition. If you don't have  them, ask the person who programmed your DWI sequence and get the files  that are specific to your use case.

 * The process can take up to 30min. At the end, a new file '''DTI-EIG'''  appears in the database (DTI=diffusion tensors images, EIG=eigenvalue). This file contains 12 volumes, ie. 12 values  for each voxel. From 1 to 9: components of the three eigenvectors; from  10 to 12:  the values of their norm to the eigenvalue.  <<BR>><<BR>> {{attachment:importDTI.gif}}

== FEM mesh ==
The FEM approach requires a segmentation of the head volume in different  tissues, represented as hexahedral or tetrahedral 3D meshes. The  methods available within Brainstorm are listed in the tutorial [[https://neuroimage.usc.edu/brainstorm/Tutorials/FemMesh|FEM mesh generation]].

Here we illustrate only the use of '''Brain2mesh''': this is not the most accurate solution for MRI segmentation but it is probably the fastest option to obtain a tetrahedral mesh of the head with 5 tissues (gray matter, white matter, CSF, skull, skin).

Note that for most other Brainstorm use cases (e.g. cortically-constrained source reconstruction), we recommend using '''SimNIBS with T1+T2''', as illustrated in the tutorial [[https://neuroimage.usc.edu/brainstorm/Tutorials/FemMedianNerveCharm#FEM_mesh_with_SimNIBS|FEM median nerve example]].

 * Right-click on the T1 MRI > MRI segmentation > '''Generate FEM mesh''' > '''Brain2mesh'''.<<BR>><<BR>> {{attachment:femMesh1.gif}}
 * After less than 15 minutes, you will obtain a new FEM mesh in the database.<<BR>><<BR>> {{attachment:femMesh2.gif}}

== FEM conductivity tensors ==
Once the FEM mesh and the DTI tensors are available in the Brainstorm database, the next step is to compute the conductivity tensor for each of the FEM mesh element.

 * Right-click on the FEM mesh > '''Compute FEM tensors'''.
 * Brainstorm checks the available tissues in the FEM mesh and assigns a default '''isotropic conductivity''' value of the conductivity for each compartment. Users can change these values to their own if needed.
 * When selecting "'''Anisotropic'''" for a tissue, the DTI values are used to generate conductivity tensors: this is used mostly for the '''white matter''' (and in some cases for the grey matter). Note that DWI can be used only for the brain tissues and not for the outers compartments (skull and skin). The methods available are:
  * '''EMA''': Effective Medium Approach: See [[http://www.pnas.org/content/98/20/11697|(Tuch 2001)]].
  * '''EMA + VC''': EMA with volume constraints: The final tensors are constrained to fit the volume of the equivalent isotropic tensor volume.
  * '''Simulated''': Artificial anisotropy (DTI is not required)
 * For this example, select: WM:Anisotropic and Isotropic for the other tissues, EMA+VA.<<BR>><<BR>> {{attachment:computeTensors.gif}}
 * Depending on the mesh resolution, this step can take few minutes (2min to 10min), in our example here, this step takes about 2min
 * The FEM conductivity tensors are saved in the FEM mesh file, in the field Tensors.<<BR>><<BR>> {{attachment:femContents.gif}}

== Visualization ==
 * Right-clicking on the FEM head > '''Display FEM tensors'''. The tensors can be displayed as ellipsoids or arrows (vectors in the direction of the main eigenvector) and overlaid on the MRI slices or the FEM mesh. <<BR>><<BR>> {{attachment:displayMenu.gif}}
 * On the FEM mesh: left=ellipsoids, right=arrows<<BR>><<BR>> {{attachment:femDisp1.gif}}
 * On the 3D MRI slices: left=ellipsoids, right=arrows: <<BR>><<BR>> {{attachment:femDisp2.gif}}
 * The orientation of the tensor is color-coded as follows:
  * '''Red''': right-left
  * '''Green''': anterior-posterior
  * '''Blue''': superior-inferior
 * To configure the display:
  * Right-click on the figure > '''FEM tensors'''.
  * Use the '''Resect sliders''' in the Surface to move the slices (or right-click+move for 3D MRI)
  * Use the keyboard shortcuts '''Up''' and '''Down''' to change the size of the graphical elements representing the tensors.

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

<<TAG(Advanced)>>

== Simulated conductivity tensors ==
When the DWI is not available, or to evaluate the effect of the conductivity change on the head model, artificial conductivities can be used using the third option '''Simulated'''. Two approaches are integrated within Brainstorm: '''Wang''' or '''Volume''' constraint. 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]'''.

 * Right-click on the FEM mesh > '''Compute FEM tensors'''.
 * Keep all the tissue as isotropic except the skull, configure as below: <<BR>><<BR>> {{attachment:simul1.gif}}

 * Below: eigenvalues parallel (longitudinal) and perpendicular (transverse) to the fiber directions for 1:10 anisotropy (transversal:longitudinal).<<BR>><<BR>> {{attachment:simul2.gif}}

== Additional documentation ==
==== Related tutorials ====
 * [[https://neuroimage.usc.edu/brainstorm/Tutorials/Duneuro|Realistic head model: FEM with DUNEuro]]
 * [[https://neuroimage.usc.edu/brainstorm/Tutorials/FemMesh|FEM mesh generation]]
 * [[https://neuroimage.usc.edu/brainstorm/Tutorials/FemTensors|FEM tensors estimation]]

==== Articles ====
 * Medani T, Garcia-Prieto J, Tadel F, Schrader S, Joshi A, Engwer C, Wolters CH, Mosher JC, Leahy RM, [[https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11595/1159554/Realistic-head-modeling-of-electromagnetic-brain-activity--an-integrated/10.1117/12.2580935.short?SSO=1|Realistic head modeling of electromagnetic brain activity: An integrated Brainstorm pipeline from MRI data to the FEM solution]] ([[http://arxiv.org/abs/2011.01292|preprint]]), '''SPIE Medical Imaging''', 2021
 * Tuch DS, Wedeen VJ, Dale AM, George JS, Belliveau JW, [[https://www.pnas.org/content/98/20/11697|Conductivity tensor mapping of the human brain using diffusion tensor MRI]], '''PNAS''', 2001