MRIQC is an open-source project, developed under the following software engineering principles:
- Modularity and integrability: MRIQC implements a nipype workflow to integrate modular sub-workflows that rely upon third party software toolboxes such as FSL, ANTs and AFNI.
- Minimal preprocessing: the MRIQC workflows should be as minimal as possible to estimate the IQMs on the original data or their minimally processed derivatives.
- Interoperability and standards: MRIQC follows the the brain imaging data structure (BIDS), and it adopts the BIDS-App standard.
- Reliability and robustness: the software undergoes frequent vetting sprints by testing its robustness against data variability (acquisition parameters, physiological differences, etc.) using images from OpenfMRI. Its reliability is permanently checked and maintained with CircleCI.
MRIQC is part of the MRI image analysis and reproducibility platform offered by the CRN. This pipeline derives from, and is heavily influenced by, the PCP Quality Assessment Protocol.
When using MRIQC, please include the following citation:
Esteban O, Birman D, Schaer M, Koyejo OO, Poldrack RA, Gorgolewski KJ; MRIQC: Advancing the Automatic Prediction of Image Quality in MRI from Unseen Sites; bioRxiv 111294; doi:10.1101/111294.
An additional resource to check out is our recent poster at OHBM:
Esteban O, Gorgolewski K and Poldrack R. MRIQC: automatic prediction of quality and visual reporting of MRI scans. F1000Research 2017, 6:1128 (poster). doi: 10.7490/f1000research.1114419.1.
Support and communication¶
The documentation of this project is found here: http://mriqc.readthedocs.io/.
Users can get help using the mriqc-users google group.
All bugs, concerns and enhancement requests for this software can be submitted here: https://github.com/poldracklab/mriqc/issues.
We use the 3-clause BSD license; the full license is in the file
All trademarks referenced herein are property of their respective holders.
Copyright (c) 2015-2017, the mriqc developers and the CRN. All rights reserved.