Diffusion Quality Control (diffQC) BIDS App

Development

User this command to build/rebuild docker container:

docker build -t diff_qc diffQC/ -m 4 GB

Run participant-level analysis with e.g. testbench bids-dataset:

docker run -ti --rm \
	-v ~/polybox/bids_testbench:/input \
	-v ~/polybox/bids_test_out:/output \
	diff_qc \
	/input /output participant

Description

This BIDS-app performs quality estimations of MRI Diffusion datasets. It is still under development, please report any issues.

Documentation

So far denoising and dwi2tensor is performed in MRtrix3.

How to report errors

Provide instructions for users on how to get help and report errors.

Acknowledgements

When using diffQC, please use cite the following work:

denoising -> Veraart2016 Gibbs ringing removal (Kellner et al., 2016) dwi2tensor -> Veraart 2014

Kellner, E.; Dhital, B.; Kiselev, V. G.; Reisert, M. Gibbs-ringing artifact removal based on local subvoxel-shifts. Magnetic Resonance in Medicine, 2006, 76(5), 1574-1581
Veraart, J.; Sijbers, J.; Sunaert, S.; Leemans, A. & Jeurissen, B. Weighted linear least squares estimation of diffusion MRI parameters: strengths, limitations, and pitfalls. NeuroImage, 2013, 81, 335-346
Veraart, J.; Fieremans, E. & Novikov, D.S. Diffusion MRI noise mapping using random matrix theory Magn. Res. Med., 2016, early view, doi:10.1002/mrm.26059

Usage

The quality examination requires the data to be organzied according to the BIDS specifications. This App has the following command line arguments:

	usage: run.py [-h]
	              [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
	              bids_dir output_dir {participant,group}

	Example BIDS App entry point script.

	positional arguments:
	  bids_dir              The directory with the input dataset formatted
	                        according to the BIDS standard.
	  output_dir            The directory where the output files should be stored.
	                        If you are running a group level analysis, this folder
	                        should be prepopulated with the results of
	                        the participant level analysis.
	  {participant,group}   Level of the analysis that will be performed. Multiple
	                        participant level analyses can be run independently
	                        (in parallel).

	optional arguments:
	  -h, --help            show this help message and exit
	  --participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]
	                        The label(s) of the participant(s) that should be
	                        analyzed. The label corresponds to
	                        sub-<participant_label> from the BIDS spec (so it does
	                        not include "sub-"). If this parameter is not provided
	                        all subjects will be analyzed. Multiple participants
	                        can be specified with a space separated list.

To run it in participant level mode (for one participant):

docker run -i --rm \
	-v /Users/filo/data/ds005:/bids_dataset:ro \
	-v /Users/filo/outputs:/outputs \
	bids/example \
	/bids_dataset /outputs participant --participant_label 01

After doing this for all subjects (potentially in parallel), the group level analysis can be run:

docker run -i --rm \
	-v /Users/filo/data/ds005:/bids_dataset:ro \
	-v /Users/filo/outputs:/outputs \
	bids/example \
	/bids_dataset /outputs group

Special considerations

Describe whether your app has any special requirements. For example:

• Multiple map reduce steps (participant, group, participant2, group2 etc.)
• Unusual memory requirements
• etc.