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<p>Quality control (QC) of magnetic resonance imaging (MRI) data before preprocessing is fundamental, because substandard data are known to introduce additional variability in the form of noise to subsequent analyses. This can result in spurious results of a false effect or the obstruction of a true effect. Consequently, there is a need for a reliable and robust method to identify subpar images, given pre-specified exclusion criteria. Here, we describe how to carry out the visual assessment of T1-weighted, T2-weighted, functional and diffusion MRI scans of the human brain with visual reports generated by MRIQC (<a href="https://mriqc.readthedocs.io/en/stable/">https://mriqc.readthedocs.io/en/stable/</a>). We provide guidance and instructions for using the MRIQC software on all the images of the input dataset using typical research settings (i.e., a high-performance computing cluster). This includes installing MRIQC, configuring datasets (30–45 min active, plus 10–15 min of compute time per scan) and executing MRIQC (10–15 min compute time per scan). We then describe how to screen the visual reports generated with MRIQC to identify artifacts and potential quality issues and annotate the latter with the ‘rating widget’, a utility that enables rapid annotation and minimizes bookkeeping errors (1–5 min per participant). Integrating proper QC checks on the unprocessed data is fundamental to producing reliable statistical results and crucial to identifying faults in the scanning settings, preempting the acquisition of large datasets with persistent artifacts that should have been addressed as they emerged.</p>

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Quality assessment and control of unprocessed anatomical, functional and diffusion MRI of the human brain using MRIQC

  • 期刊论文

  • 发表日期：

    2026年5月11日

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  • McKenzie P. Hagen,
  • Céline Provins,
  • Eilidh MacNicol,
  • Jamie K. Li,
  • Teresa Gomez,
  • Mélanie Garcia,
  • Saren H. Seeley,
  • Jon Haitz Legarreta,
  • Martin Norgaard,
  • Patrick G. Bissett,
  • Russell A. Poldrack,
  • Ariel Rokem,
  • Oscar Esteban

摘要

Quality control (QC) of magnetic resonance imaging (MRI) data before preprocessing is fundamental, because substandard data are known to introduce additional variability in the form of noise to subsequent analyses. This can result in spurious results of a false effect or the obstruction of a true effect. Consequently, there is a need for a reliable and robust method to identify subpar images, given pre-specified exclusion criteria. Here, we describe how to carry out the visual assessment of T1-weighted, T2-weighted, functional and diffusion MRI scans of the human brain with visual reports generated by MRIQC (https://mriqc.readthedocs.io/en/stable/). We provide guidance and instructions for using the MRIQC software on all the images of the input dataset using typical research settings (i.e., a high-performance computing cluster). This includes installing MRIQC, configuring datasets (30–45 min active, plus 10–15 min of compute time per scan) and executing MRIQC (10–15 min compute time per scan). We then describe how to screen the visual reports generated with MRIQC to identify artifacts and potential quality issues and annotate the latter with the ‘rating widget’, a utility that enables rapid annotation and minimizes bookkeeping errors (1–5 min per participant). Integrating proper QC checks on the unprocessed data is fundamental to producing reliable statistical results and crucial to identifying faults in the scanning settings, preempting the acquisition of large datasets with persistent artifacts that should have been addressed as they emerged.