Page Last Updated: May 15, 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.

Location in Release Data🔗

Raw data QC metrics are provided in the session-level scans TSV Files. QC metrics included in the scans TSV file are summarized here.

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 & Completeness Checks â–¸

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.

Automated QC Metrics â–¸
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.

Manual QC Metrics â–¸
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🔗

Data Warning â–¸

The following groups are missing all or a large portion of BrainSwipes QC results in the release data:

  • V02 sessions processed with T1-based surface reconstruction (Infant FreeSurfer method) within Infant fMRIPrep: ~70% of the visual reports across sessions are missing BrainSwipes QC scores. Note, however, that for separate reasons we advise against using this data for analyses - see Data Warning.
  • V02 sessions with only a T2w anatomical image present (that passes raw data QC), and no T1w: missing ALL BrainSwipes QC results in the release data.

Completed tabulated data can be found in the HBCD Private Release Notes accessible to DUC-authorized users.

  Download Completed BrainSwipes Results

QC is performed on processed structural and functional MRI data via manual review of XCP-D visual reports. Though manual inspection remains the gold standard for QC, it is highly resource-intensive. Manual visual review was therefore performed using BrainSwipes, a gamified crowdsourcing platform where users classify images as Pass or Fail by swiping right or left after completing a brief visual QC tutorial.

BrainSwipes QC results were also used to inform processed data exclusion (see Processed Data Exclusion Criteria for details).

Example quality assessment of surface delineation on BrainSwipes platform (displaying brain in axial plane at level of basal ganglia/putamen).

Detailed BrainSwipes QC Procedures â–¸

Surface Delineation
For structural QA, swipers are presented with image slices in coronal, axial, and sagittal planes to assess the accuracy of T1w and T2w surface delineations in differentiating gray and white matter. Images are derived from XCP-D visual reports.

Atlas Registration
In addition to surface delineation, structural QA also includes atlas registration quality, evaluated by overlaying delineations of the subject’s image onto the atlas, and vice versa. Swipes display nine T1w slices for visual inspection, with three slices per anatomical plane. Quality is assessed based on the alignment of the outer boundaries of the overlaid contours with those of the underlying image, ensuring minimal gaps or misalignments. Images are derived from XCP-D visual reports.

Functional Registration
Functional registration is evaluated by overlaying outlines of functional images onto structural images and vice versa. Swipes display nine slices of the same functional image for visual inspection, with three slices per anatomical plane. Quality is assessed similarly to structural atlas registration, focusing on the alignment of the overlaid contours. Additional evaluation includes checking for artifacts such as signal dropout. Images are derived from XCP-D visual reports.

Location in Release Data🔗

BrainSwipes QC results for processed data are provided as tabulated data (img_brainswipes_xcpd_*). BrainSwipes presents users with a series of visual reports, generated by XCP-D, to assess the quality of structural and functional processing. Each report is independently rated as Pass (1) or Fail (0). The BrainSwipes data include:

  • The mean QC score and number of reviewers for each individual visual report
  • The mean QC score and average number of reviewers across all visual reports
References â–¸

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