Prediction of response to VNS in DRE

The aim of this thesis is the understand which microstructural features differ between responders and non-responders to Vagus Nerve Stimulation (VNS) in Drug-Resistant Epileptic (DRE) patients.

The dataset we have is made up of 19 patients, for each we have T1, T2 and DWI volumes of their brain after the implantation of the VNS.

Setup environment

Setup environment on your own computer

This thesis was composed of different tools and software, which are listed in .config_envs/enviroment.yml or .config_envs/requirements.txt, while others are specified in Dockerfile.

A non esaustive list is reported here:

• FreeSurfer
  • TRACULA
• Elikopy
• FSL
• Microstructure Fingerprinting
• ANTs
• MRtrix3
• Radiomics
• PyTorch

To set up all the environments without going mad a Docker image was created with the ready environment.

You can download the image from Docker Hub through:

docker pull micerr/epilepsy-dmri-vns:1.0.0

The same image can be built using the Dockerfile, and it takes ~1 hour.

docker build -t micerr/epilepsy-dmri-vns /path/to/project/folder/

Inside the Dockerfile is possible to change the version of FreeSurfer or update the version of Elikopy.

Then create and run a container:

docker run -i -v /absolute/path/to/Epilepsy-dMRI-VNS/:/root/Epilepsy-dMRI-VNS/ -t micerr/epilepsy-dmri-vns

As soon as it ends, you have to activate the environment inside the container with:

conda activate dMRI

If everything works the ~\Epilepsy-dMRI-VNS folder in the container will be the same as your folder.

Setup environment on CECI

Many of the software and libraries are already installed on CECI cluster.

• Install Elikopy on CECI: elikopy docs

During the installation of Elikopy on the clusters in /CECI/proj/pilab/Software/config_elikopy.bash are loaded different modules:

• MRtrix3
• MisterI
• ANTs
• DIAMOND
• FSL
• FreeSurfer
• C3D
• Microstructure Fingerprinting
• Elikopy

The other software can be installed following the CECI docs on pre-installed software: CECI.

To install software that is not in the pre-installed software follow always the CECI docs.

dMRI preprocessing with ElikoPy

The preprocessing of diffusion images is done by using ElikoPy. The full documentation is here, while the repository is here. It's maintained by the amazing guys of PiLAB-Medical-Imaging of UCLouvain.

I suggest reading all the documentation before using my Python script and taking it as an example. The parameters used in the preprocessing and metric models highly depend on the study that you are doing. Many new parameters and changes are done every month by the team, so take it with a grain of salt.

Read the script in src/0-dMRI-proproc/preproc.py, it is full of comments to drive you in the understanding.

preproc.py takes the parameter -f which is the relative path to the study folder (how the study folder must be composed is explained in Elikopy docs ). It takes also the parameter -CECI if it runs of CECI cluster.

In this step must be present at least the folder study/data_1/ where are stored:

• acqparams.txt
• index.txt
• subj0.bval
• subj0.bvec
• subj0.json
• subj0.nii.gz
• subj1.bval
• ...
• subjN.nii.gz

If everything is set you can run the Python script

python src/0-dMRIpreproc/preproc.py -f ./study/

Brain segmentation with FreeSurfer

Brain segmentation is used to extract the Region of Interest (ROI) from T1 images.

Two scripts are present sub_seg.py for CECI users, to segment the brain on the cluster, seg.sh to run the segmentation on your machine.

The two scripts run automatically in parallel with the segmentation to speed up the process. Each brain segmentation can take from 8 to 10 hours, therefore parallelizing is necessary.

In seg.sh the number of jobs in parallel can be set. I always suggest njobs=nsubjects to reduce the time to a maximum of 10 hours.

The T1 volumes should be located in study/T1/. Furthermore, to increase the accuracy of the segmentation you can use the T2 volumes during the computation with -T2 and -T2pial parameters.

• Documentation of recon-all command here
• A very good tutorial on FreeSurfer segmentation here

A further more precise segmentation of the Thalamus can be done by using segmentThalamicNuclei.sh explained in the FreeSurfer docs. The team of FreeSurfer is working on it and soon this tool will be implemented directly in FreeSurfer.

Read the comments on src/1-brainSegFreeSurfer/sub-seg for more hints.