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Updated: May 13, 2026
High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
Published on: May 10, 2012
Quality assessment and control of unprocessed anatomical, functional and diffusion MRI of the human brain using MRIQC
McKenzie P Hagen1, Céline Provins2, Eilidh MacNicol3
1Department of Psychology, University of Washington, Seattle, WA, USA. mphagen@uw.edu.
Abstract:
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.
Insights
Implementing quality control (QC) for magnetic resonance imaging (MRI) data using MRIQC software is essential. This method ensures reliable neuroimaging analysis by identifying and excluding substandard scans before preprocessing.
Area of Science:
- Neuroimaging
- Data Science
- Medical Imaging Analysis
Background:
- Substandard magnetic resonance imaging (MRI) data introduce noise and variability, potentially leading to spurious or obscured analytical results.
- Robust quality control (QC) methods are crucial for identifying and excluding subpar MRI scans prior to preprocessing.
- Current methods may lack efficiency or reliability in identifying image artifacts.
Purpose of the Study:
- To describe a reliable and robust method for visual assessment of MRI data quality using the MRIQC software.
- To provide guidance on installing, configuring, and executing MRIQC for various MRI scan types (T1w, T2w, fMRI, dMRI).
- To detail the process of screening MRIQC reports and annotating quality issues using the 'rating widget'.
Main Methods:
- Utilized MRIQC software for automated visual assessment of T1-weighted, T2-weighted, functional, and diffusion MRI scans.
- Provided instructions for MRIQC installation and dataset configuration on high-performance computing clusters.
- Described screening of generated visual reports and annotation using the 'rating widget' for efficient quality assessment.
Main Results:
- MRIQC generates visual reports that facilitate the identification of artifacts and quality issues in MRI data.
- The 'rating widget' enables rapid and accurate annotation of scan quality, minimizing bookkeeping errors.
- The described workflow integrates seamlessly into typical research settings, enhancing QC efficiency.
Conclusions:
- Integrating MRIQC for visual assessment of raw MRI data is fundamental for reliable neuroimaging analysis.
- This approach helps identify scanning setting faults early, preventing the acquisition of large datasets with persistent artifacts.
- Ensuring high-quality unprocessed data is critical for reproducible and valid statistical outcomes in neuroimaging research.
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