# Neuro

In the last article I introduced Extended Streamline Tractography (XST). In this article I will talk talk about the best practices and supporting softwares when applying XST to real MR data.

## The Pipline

1. Preprocess DWI images
2. Generate seeding coordinates from ROI mask
3. Running XST
4. Embedd diffusivity metrics onto the tractography model

### Requirements

The following are required to run the supporting scripts:

#### PyNRRD:

git clone https://github.com/sinkpoint/pynrrd.git
python setup.py install


#### Scripts:

git clone https://github.com/sinkpoint/neuro-scripts.git


Then make sure to add the neuro-script repo dir to the path in .bashrc. If you are on OSX then add it to .bash_profile.

export path=\$PATH:/my/neuro-script


### Preprocess DWI images

Diffusion weighted image (DWI) preprocessing involves a number of steps, including motion and eddy-current corrections, possibly reverse-phase encoding corrections, resampling, etc, that are outside the scope of this article. Here I’ll outline the XST specific steps. For for information on general DWI preprocessing, refer to the FSL EDDY and TOPUP documentations.

# Intro to Extended Streamline Tractography

neuro | Created 2015-06-17 00:00:00 -0400

Diffusion tensor imaging (DTI) has become a mainstay in today’s neuroimaging research toolbox. It is essential for in vivo study of white matter pathways in structural neuroimaging. It is however hampered by its inability to resolve crossing fibers within key brain regions. This limitation is due to the DTI gaussian diffusion model, where regions of low anisotropy and cross-fiber regions are indistinguishable due to their gaussian diffusion profile.

A number of approaches has appeared over the years to better image crossing regions. Most of these approaches are based on diffusion acquistions of more than 50 directions, and therefore belongs to the category of high angular resolution diffusion imaging (HARDI). Newer approaches tend to fall into model-free or model-based methods. Where model-free methods attempts to estimate a number of direction maximums directly from the diffusion signal, while model-based methods attempts to estimate a set number (generally less than 3) of fiber directions by simplifying the underlying diffusion assumptions. Due to the fact that these methods are all trying to estimate multiple fiber directions, sometimes they are called multi-tensor methods, although the term is not exacly correct, as a number of these methods do not estimate tensors at all.

Due to the higher complexity of model-free methods, they are sensitive to acquisition quality. Often times they rely on a different sampling scheme in q-space, such as cartesian grid sampling in diffusion spectral imaging (DSI), or more popularly radial sampling on single or multiple concentric spherical shells such as in Q-ball and Constrined Spherical Deconvolution (CSD).