I think that the lowest level of involvement will be to learn how to launch the pipeline and be responsible of testing and visualizing results. The intermediate level of involvement will correspond on coding the data organization and container launching scripts. The highest level of involvement will be coding the Python and Matlab codes within the container so that it can read qMRI data and provide the tract specific metrics. The three levels or involvement will run in parallel. First milestone will be to plan what each parallel line will need to accomplish and how (so training on the tool will be given). The second milestone will be to do the actual coding. The third milestone will be to combine the three branches for testing and improving it iteratively until it is working.
What will participants learn:
What is diffusion imaging and how it works. qMRI. Running a diffusion pipeline from dicom images to tract metrics. Containerization technology (Docker and Singularity). Python and Matlab. Git (working with a branch within a fork and creating a pull request)
Number of collaborators:
3
Credit to collaborators:
They will be in a contributing page in the main repo.
Type of project:
Pipeline development
Programming languages:
Matlab, Python, Containerization (Docker and Singularity)
Necessary git skills level for the project:
Basic (commit & push)
Modality:
DWI, MRI, qMRI
Software suites:
AFNI, ANTs, BIDS, fMRIPrep, Freesurfer, FSL, MRtrix, the pipeline uses many different softwares...
Project title:
RTP2: Reproducible Tract Profiles
Project leader:
Garikoitz Lerma-Usabiaga
Email:
garikoitz@gmail.com
Collaborators:
Mengxing Liu
Brainhack Global 2022 Event:
BrainHack Donostia
Topic:
Diffusion
What is the purpose of the project:
Combining quantitative MRI (qMRI) maps with white matter tracts.
Where can participants find key resources to work on this project:
https://github.com/garikoitz/RTP-pipeline
What stage is the project on:
4
Required programming skills for the project:
Tasks for all levels
Background knowledge needed on the topic:
Basic
Data to use:
We will select some already preprocessed data so that we can only test the qMRI part.
Link to project repository:
https://github.com/garikoitz/RTP-pipeline
Goals for Brainhack Donostia 2022:
I think that the lowest level of involvement will be to learn how to launch the pipeline and be responsible of testing and visualizing results. The intermediate level of involvement will correspond on coding the data organization and container launching scripts. The highest level of involvement will be coding the Python and Matlab codes within the container so that it can read qMRI data and provide the tract specific metrics. The three levels or involvement will run in parallel. First milestone will be to plan what each parallel line will need to accomplish and how (so training on the tool will be given). The second milestone will be to do the actual coding. The third milestone will be to combine the three branches for testing and improving it iteratively until it is working.
What will participants learn:
What is diffusion imaging and how it works. qMRI. Running a diffusion pipeline from dicom images to tract metrics. Containerization technology (Docker and Singularity). Python and Matlab. Git (working with a branch within a fork and creating a pull request)
Number of collaborators:
3
Credit to collaborators:
They will be in a contributing page in the main repo.
Type of project:
Pipeline development
Programming languages:
Matlab, Python, Containerization (Docker and Singularity)
Necessary git skills level for the project:
Basic (commit & push)
Modality:
DWI, MRI, qMRI
Software suites:
AFNI, ANTs, BIDS, fMRIPrep, Freesurfer, FSL, MRtrix, the pipeline uses many different softwares...