QA/QC of the unprocessed anatomical, functional, and diffusion MRI data of the Human Connectome PHantom (HCPh) dataset with MRIQC

Robust quality assessment and quality control (QA/QC) protocols are now widely considered key to ensuring the reliability of neuroimaging analyses. Despite the recognized importance of such protocols, their practical implementation remains challenging. We propose conceptualizing QA/QC as a Swiss cheese security model, in which several layers (QC checkpoints) are established along the neuroimaging pipeline with pre-defined exclusion criteria to ensure no subpar images reach the analysis step. In this preprint, we demonstrate the first layer of such an approach (i.e., unprocessed data) on a mid-sized, single-subject dataset leveraging MRIQC for efficient screening. Utilizing MRIQC's visual reports and its rating widget, two experts assigned quality ratings to individual images across modalities (anatomical, functional and diffusion). Moreover, we recalibrated MRIQC's random-forests classifier to assist in the exclude/include decision of T1w images. By formalizing exclusion criteria, generating quality reports, and re-calibrating classifiers, we have established a valuable foundation for improving data quality and reliability in neuroimaging research.