A modular, portable, reusable and reproducible processing pipeline software for fetal brain MRI super-resolution

The essence of this project is to develop a modular BIDS App for fetal brain MRI super-resolution that can interface with the open-source C++ Medical Image Analysis Laboratory Super-Resolution ToolKit (MIALSRTK), a set of tools compiled and distributed as a Docker image, that provides a complete solution to the processing pipeline.

This project is built on standards, tools, and frameworks developed to address the reproducibility and transparency challenges in the neuroimaging field :

• the Brain Imaging Data Structure (BIDS), a standard to organize and describe neuroimaging data,
• the Nipype dataflow library, which provides a common python API to interface with different tools and a workflow engine which control tool execution and record provenance.
• and the BIDS App framework, which promotes reproducibility and portability.

It investigates a novel framework, the Self-contained Interfaceable No-nonsense Application (SINAPP) framework, designed to provide modularity and reusability of the pipeline stages within the BIDS App framework.

Achievements during the previous Brainhack

Brainhack Open Geneva 2019 - March Friday 22nd and Saturday 23rd 2019

Project 03: Package a portable and reproducible software for motion-robust MRI super-resolution

Team: Seirios, Brenda, Snezana, Niloufar and Sébastien

Work done:

• Creation of notebook/brainhack.ipnb
• Implement a Nipype interface that calls the NLMdenoising program from the containerized MIALSRTK library
• Implement simple workflow that uses the BIDSDataGrabber interface to load data, which is then connected to the NLMdenoising interface

See the final

Brainhack Global 2019 - November Thursday 7th, Friday 8th and Saturday 9th 2019

Project 05: SINAPP (Modular BIDS App) for Motion-robust Super-Resolution MRI

Team: Priscille, Hamza, Manik, Olivier, Guillaume and Sébastien

Work done:

• Refactored code for building the project as a python package pymialsrtk
• Implement the pipeline for the NLMdenoising SINAPP which BIDS App parser
• Creation of the SINAPP Dockerfile for pipeline containerization
• Adopt Docker-out-of-Docker solution to run within a first container (the SINAPP Docker container) the NLMdenoising program contained in a second container (the MIALSRTK docker image)
• Start integration and adaption of notebooks and interfaces implemented by Priscille and Hamza without the Docker framework (interface that calls directly the MIALSRTK program and not the program in the containerized MIALSRTK library)

See the final

Bug and support

This project is conducted with transparency in mind. Please use GitHub Issues (https://github.com/brainhack-ch/superres-mri/issues) to report and and discuss bugs and new features.

Installation

• Clone this repository:

git clone https://github.com/brainhack-ch/superres-mri.git <Your/installation/dir>

• Install the supermri-env conda environment with all the packages and dependencies:

conda env create -f environment.yml

• Download the latest version of the mialsuperresolutiontoolkit Docker image:

docker pull sebastientourbier/mialsuperresolutiontoolkit:latest

Run the SINAPP example

This example of SINAPP that performs NLM denoising was developed during the BrainHack Global Geneva 2019 and provides a proof of feasibility of the envisioned framework. Details about the solutions found to run a docker inside an other container are available at #5. Its architecture is illustrated by the figure below:

We provide two scripts in sinapps/example_scripts that facilitates the built and testing of this example. The script build_sinapp-core_and_sinapp-nlmdenoise.shshows you how we build the sinapp-nlmdenoise docker image. The script run_sinapp-nlmdenoise.sh shows you how to execute it.

Instructions

• Edit the path defined by superes-mri_dir in build_sinapp-core_and_sinapp-nlmdenoise.sh by your <Your/installation/dir>.
• Edit the path defined by bids_dir in the run_sinapp-nlmdenoise.sh script to the location of your BIDS dataset location (<Your/BIDS/dir>)
• In a terminal go to your <Your/installation/dir>:

cd <Your/installation/dir>

• Build the SINAPP:

sh sinapps/example_scripts/build_sinapp-core_and_sinapp-nlmdenoise.sh

• Run the SINAPP on all T2w scans of sub-01 in <Your/BIDS/dir>:

sh sinapps/example_scripts/run_sinapp-nlmdenoise.sh

The results are generated in <Your/BIDS/dir>/derivatives/superres-mri/sub-01/nipype/sinapp_nlmdenoise.

Want to help with the development of the entire collection of SINAPPs?

Please check the github issue #7 dedicated to the management, tracking, and discussions about the developement of the SINAPP collection for the MIALSRTK library.

Running the notebooks

• Go to the notebooks directory in the cloned repo:

cd <Your/installation/dir>/notebooks

• Activate the conda environment supermri-env:

conda activate supermri-env

• Launch the ipython notebook server:

jupyter notebook

License

This project is licensed under the BSD 3-Clause License - see the LICENSE file for details

Contributors

• Priscille
• Hamza
• Manik
• Olivier
• Guillaume
• Seirios
• Brenda
• Snezana
• Niloufar

Instigators

• Sebastien Tourbier
• Michael Dayan

See also the list of contributors who participated in this project.