Skip to main content

Automated Quality Control and visual reports for Quality Assessment of structural (T1w, T2w) and functional MRI of the brain.

Project description

DOI Zenodo Package Pythons DevStatus License Documentation CircleCI

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 FSL, 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: 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/nipreps/mriqc/issues.

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 http://www.apache.org/licenses/LICENSE-2.0.

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).

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mriqc-23.0.0rc0.tar.gz (10.3 MB view details)

Uploaded Source

Built Distribution

mriqc-23.0.0rc0-py3-none-any.whl (315.2 kB view details)

Uploaded Python 3

File details

Details for the file mriqc-23.0.0rc0.tar.gz.

File metadata

  • Download URL: mriqc-23.0.0rc0.tar.gz
  • Upload date:
  • Size: 10.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.5

File hashes

Hashes for mriqc-23.0.0rc0.tar.gz
Algorithm Hash digest
SHA256 07e68728232483009fc3bbdfece619a32da6ea2ffd20b49b04687fad7ece4016
MD5 f4128d59e67349b56e7ec62bbc40bec6
BLAKE2b-256 ada724974a6ec32a6e690a5ba60debe0c6793464ddfe1f5eec887a0cc3a94b73

See more details on using hashes here.

File details

Details for the file mriqc-23.0.0rc0-py3-none-any.whl.

File metadata

  • Download URL: mriqc-23.0.0rc0-py3-none-any.whl
  • Upload date:
  • Size: 315.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.5

File hashes

Hashes for mriqc-23.0.0rc0-py3-none-any.whl
Algorithm Hash digest
SHA256 7c15ec099371c6eb0b8937c8b4b309ecb970fa084f19fae00566e51059f5939f
MD5 26461b8d54bb29ff25e8ae9586a02d95
BLAKE2b-256 19b0b7c8ca88b41bf7eb1306da04d20ea181a3f8e27347e90b70c5d3a2d84ebd

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page