Page Last Updated: June 20, 2026
HBCD MR Quality Control Procedures🔗
Raw MR Data QC🔗
Raw MRI QC combines automated and manual checks to evaluate unprocessed data and identify acquisition errors, image artifacts, or corrupted files before downstream processing. Automated QC is applied to all data. Due to the large data volume and time-intensive nature of manual inspection, manual visual review is only performed for series that fail automated QC. Although automated tools detect most quality issues, some artifacts may be missed if misclassified or not assessed as part of automated QC.
Automated QC🔗
Automated QC begins immediately after data upload with protocol compliance and completeness checks (expand infobox for details). Data that fail are flagged for review and excluded from release until resolved. For compliant data, automated QC metrics are then calculated (see table below).
Protocol compliance is performed by extracting imaging parameters from DICOM headers to confirm that key parameters (e.g., voxel size, TR, orientation) match the expected protocol for each scanner. Out-of-compliance series are flagged for review and sites are contacted if corrective action is needed.
Completeness checks verify that all expected series are present in each imaging session. Missing data usually indicate an aborted scan or incomplete data transfer. Series included in a valid session include: T1w & T2w structural scans; 2 resting state functional runs (each accompanied by fieldmaps acquired in AP and PA phase encoding directions); diffusion scans (acquired both AP and PA); quantitative QALAS and B1 maps; and an MRS scan and SVS localizer.
| Modality | Automated QC Metrics |
|---|---|
| sMRI & qMRI | • Estimate motion artifacts using a deep learning model • Compute signal-to-noise ratio (SNR) |
| fMRI |
• Estimate head motion with average FDframewise displacement and data (sec) at FD thresholds of 0.2/0.3/0.4 mm (Power et al., 2012) • Detect line artifacts and FOV cutoff • Compute spatial smoothness (FWHM) and temporal SNR (tSNR) after motion correction (Triantafyllou et al., 2005) |
| dMRI |
• Estimate head motion (framewise displacement, FD) • Refine motion estimates via registration to tensor-synthesized images (Hagler et al., 2009) • Identify dark slices (caused by abrupt head movements) using RMS difference between raw and tensor-fitted data • Calculate total slices and frames with motion artifacts • Detect line artifacts and field-of-view (FOV) cutoff |
| Field Maps | Detect line artifacts and field-of-view (FOV) cutoff |
| All | Compute SNR where applicable |
Manual Review🔗
Data are flagged for manual review based on automated QC results using multivariate prediction and Bayesian classifiers, so only a subset undergoes both automated and manual review. When a series is flagged, trained technicians perform visual review and rate artifact severity on a 0–3 scale: none (0), mild (1), moderate (2), or severe (3). Series rated 3 (severe) are automatically assigned an overall QC score of 0 (Fail) and excluded from downstream processing. For all others, final selection is informed by manual ratings, reviewer notes, and automated QC metrics.
| Modality | Manual QC Procedures & Scoring |
|---|---|
| sMRI | • Motion artifacts (ripples, blurring), scored 0–3 • Document additional issues (e.g., intensity inhomogeneity, ghostingfaint displaced copy of anatomy due to slices outside FOV) |
| qMRI | • Same artifact scoring (0–3) • Inspect derived data (parametric maps, ROI analysis, quantitative checks for 3D-QALAS) |
| dMRI & fMRI, & field maps |
• Score susceptibility artifacts, FOV cutoff, and horizontal line artifacts (present in the sagittal view) • Note susceptibility artifacts, including signal dropoutcommon in posterior occipital cortex of infant fMRI data acquired in PA phase encoding direction, signal bunching, and warping |
| MRS | Visual inspection and overall QC only of SVS localizer (used to define spectroscopy ROI) |
BrainSwipes🔗
BrainSwipes is a gamified crowdsourcing platform used to perform manual QC of processed MRI data. Reviewers assess images from XCP-D visual reports, displaying a series of brain images in coronal, axial, and sagittal planes, and classify each report as Pass (1) or Fail (0).
The released data include:
- Report-level QC metrics: mean QC score and number of reviewers for each visual report
- Subject-level QC metrics: mean QC score and average number of reviewers across all reports for a participant
BrainSwipes QC results were also used to inform processed-data exclusions.
| QC Assessment | What is Evaluated |
|---|---|
| Surface Delineation | Accuracy of cortical surface placement and gray/white matter boundaries |
| Atlas Registration | Alignment between the participant's anatomical image and the reference atlas |
| Functional Registration | Alignment between functional and structural images and detection of major artifacts such as signal dropout |
QC Summary Statistics🔗
Post-processing QC analyses are performed for certain MR modalities to provide in tandem with release data. These results are included on the modality README pages - see the following Quality Control Summary Statistics for:
- Functional MRI - generated from BrainSwipes QC and XCP-D connectivity matrices
- Diffusion MRI - summary statistics for automated QC metrics included in QSIPrep pipeline derivatives
Note that many of the processing pipelines provide QC metrics in their derivative outputs, including quantitative metrics, brain visualizations/visual reports, and summary figures. In some cases these are directly leveraged for data release QC procedures, such as BrainSwipes where the images used for visual QC are sourced directly from the XCP-D HTML reports. In other cases, the QC results are provided as-is to users without additional analysis, e.g. summary reports generated by Infant fMRIPrep. The MRIQC pipeline is unique in that the outputs are strictly QC metrics, run on raw BIDS data to extract image quality metrics from structural and functional MRI. Review the documentation pages for each modality for details.
Dean III, D. C., Tisdall, M. D., Wisnowski, J. L., Feczko, E., Gagoski, B., Alexander, A. L., ... & HBCD MRI Working Group. (2024). Quantifying brain development in the HEALthy Brain and Child Development (HBCD) Study: The magnetic resonance imaging and spectroscopy protocol. Developmental Cognitive Neuroscience, 70, 101452. https://doi.org/10.1016/j.dcn.2024.101452
Gard, A. M., Hyde, L. W., Heeringa, S. G., West, B. T., & Mitchell, C. (2023). Why weight? Analytic approaches for large-scale population neuroscience data. Developmental Cognitive Neuroscience, 59, 101196. https://doi.org/10.1016/j.dcn.2023.101196
Hagler, D. J., Jr, Ahmadi, M. E., Kuperman, J., Holland, D., McDonald, C. R., Halgren, E., & Dale, A. M. (2009). Automated white-matter tractography using a probabilistic diffusion tensor atlas: Application to temporal lobe epilepsy. Human Brain Mapping, 30(5), 1535–1547. https://doi.org/10.1002/hbm.20619
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage, 59(3), 2142–2154. https://doi.org/10.1016/j.neuroimage.2011.10.018
Triantafyllou, C., Hoge, R. D., Krueger, G., Wiggins, C. J., Potthast, A., Wiggins, G. C., & Wald, L. L. (2005). Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters. NeuroImage, 26(1), 243–250. https://doi.org/10.1016/j.neuroimage.2005.01.007