To help you get started, we've collected the most common ways that mriqc is being used within popular public projects.

poldracklab / mriqc / mriqc / utils / mriqc_run.py View on Github

# Check physical memory
    total_memory = opts.mem_gb
    if total_memory < 0:
        try:
            from psutil import virtual_memory
            total_memory = virtual_memory().total // (1024 ** 3) + 1
        except ImportError:
            MRIQC_LOG.warn('Total physical memory could not be estimated, using %d'
                           'GB as default', DEFAULT_MEM_GB)
            total_memory = DEFAULT_MEM_GB

    if total_memory > 0:
        av_procs = total_memory // 4
        if av_procs < 1:
            MRIQC_LOG.warn('Total physical memory is less than 4GB, memory allocation'
                           ' problems are likely to occur.')
            n_procs = 1
        elif n_procs > av_procs:
            n_procs = av_procs

    settings = {
        'bids_dir': bids_dir,
        'write_graph': opts.write_graph,
        'testing': opts.testing,
        'hmc_afni': opts.hmc_afni,
        'n_procs': n_procs,
        'ants_nthreads': opts.ants_nthreads,
        'output_dir': op.abspath(opts.output_dir),
        'work_dir': op.abspath(opts.work_dir),
        'verbose_reports': opts.verbose_reports or opts.testing
    }

View more ways to use mriqc

mriqc: image quality metrics for quality assessment of MRI

MRIQC extracts no-reference IQMs (image quality metrics) from structural (T1w and T2w) and functional MRI (magnetic resonance imaging) data.

MRIQC is an open-source project, developed under the following software engineering principles:

1. Modularity and integrability: MRIQC implements a nipype workflow to integrate modular sub-workflows that rely upon third party software toolboxes such as ANTs and AFNI.
2. Minimal preprocessing: the MRIQC workflows should be as minimal as possible to estimate the IQMs on the original data or their minimally processed derivatives.
3. Interoperability and standards: MRIQC follows the the brain imaging data structure (BIDS), and it adopts the BIDS-App standard.
4. 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.

Citation

Support and communication

The documentation of this project is found here: .

Users can get help using the mriqc-users google group.

All bugs, concerns and enhancement requests for this software can be submitted here: .

Development

A local development build based on the latest docker build of MRIQC can be built with this command run from the root of this repository:

docker build -f Dockerfile_devel -t mriqc_devel .

To test changes the local source code will need to be mounted into the development container:

docker run --rm -v .:/src/mriqc mriqc_devel

New Python dependencies can be added in pyproject.toml under dependencies. Any time a dependency is changed or added there the docker image will need to be rebuilt using the above docker build command.

License information

MRIQC adheres to the general licensing guidelines of the NiPreps framework.

MRIQC originally derives from, and hence is heavily influenced by, the PCP Quality Assessment Protocol. Please check the NOTICE file for further information.

License

Copyright (c) 2021, the NiPreps Developers.

As of the 21.0.x pre-release and release series, MRIQC is licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at .

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Acknowledgements

This work is steered and maintained by the NiPreps Community. The development of this resource was supported by the Laura and John Arnold Foundation (RAP and KJG), the NIBIB (R01EB020740, SSG; 1P41EB019936-01A1SSG, YOH), the NIMH (RF1MH121867, RAP, OE; R24MH114705 and R24MH117179, RAP; 1RF1MH121885 SSG), NINDS (U01NS103780, RAP), and NSF (CRCNS 1912266, YOH). OE acknowledges financial support from the SNSF Ambizione project “Uncovering the interplay of structure, function, and dynamics of brain connectivity using MRI” (grant number PZ00P2_185872).