The implementation of adequate quality assessment (QA) and quality control (QC) protocols within the magnetic resonance imaging (MRI) research workflow is resource- and time-consuming and even more so is their execution. As a result, QA/QC practices highly vary across laboratories and “MRI schools”, ranging from highly specialized knowledge spots to environments where QA/QC is considered overly onerous and costly despite evidence showing that below-standard data increase the false positive and false negative rates of the final results. Here, we demonstrate a protocol based on the visual assessment of images one-by-one with reports generated by MRIQC and fMRIPrep, for the QC of data in functional (blood-oxygen dependent-level; BOLD) MRI analyses. We particularize the proposed, open-ended scope of application to whole-brain voxel-wise analyses of BOLD to correspondingly enumerate and define the exclusion criteria applied at the QC checkpoints. We apply our protocol on a composite dataset (n = 181 subjects) drawn from open fMRI studies, resulting in the exclusion of 97% of the data (176 subjects). This high exclusion rate was expected because subjects were selected to showcase artifacts. We describe the artifacts and defects more commonly found in the dataset that justified exclusion. We moreover release all the materials we generated in this assessment and document all the QC decisions with the expectation of contributing to the standardization of these procedures and engaging in the discussion of QA/QC by the community.

Defacing (i.e. removing facial features) from structural imaging has become a necessary step before data sharing to ensure participants’ anonymity (Schwarz et al. 2021; Fig 1A). This process has proven to have some deleterious effects on the downstream research workflow (de Sitter et al. 2020). Here, we present an exploratory analysis prior to testing the hypothesis that both quality ratings by human experts and the image quality metrics (IQMs) that MRIQC (Esteban et al. 2017) extracts are affected by defacing. We found sufficient evidence on a small sample that there might be an effect. Therefore, we will pre-register and carry out a confirmatory analysis on a larger, unseen, sample.

Echo-Planar Imaging (EPI) allows very fast acquisition of whole-brain data, which enables standard functional & diffusion MRI (f/dMRI). However, EPI is notably sensitive to variations in the base B0 field. Small deviations in parts-per-million from the nominal B0 caused by steps in magnetic susceptibility (tissue interfaces) introduce misplacements in the registered location of voxels of up to some cm in standard settings along the phase-encoding direction (PE), apparent as local geometrical distortions of the imaged specimen. In humans, the susceptibility distortion (SD) is prominent starting at the petrous bone and extending towards the ear canals, defining a sort of triangle where signal vanishes (Fig. 1). SD is well-known, but existing solutions require mapping B0 deviations and are sensitive to several imaging parameters. In practice, addressing SDs is error-prone and often overlooked. Here, we introduce SDCFlows (SD Correction Flows), an open-source utility that leverages BIDS1 and several existing software tools to provide standardized, best-effort SD correction.

Magnetic resonance imaging (MRI) generates a radiofrequency field (B1) to frequency encode the object being imaged. Deviations from the nominal B1 field produce artifactual intensity nonuniformity (INU) across the image, which is problematic, especially for automated analyses that assume a tissue is represented by voxels of similar intensity throughout the image (Belaroussi et al. 2006). These artifacts are particularly exacerbated by receiver coil failures. Such events are difficult to capture as they tend to be short-lived and sporadic. In brain blood-oxygen-level-dependent (BOLD) functional MRI (fMRI), B1 field dynamics is usually visualized with a video of the scan to spot signal intensity changes, but this method is time-consuming and error-prone, as the human observer needs to keep focus during the whole video. Here, we showcase a visualization tool to assess B1 field dynamics and a derived summary metric to efficiently detect low spatial-frequency artifacts, such as transient INU.

Quality control of functional MRI data is essential as artifacts can have a critical impact on subsequent analysis. Yet, visual assessment of a dataset is tedious and time-consuming. By extending the carpet plot with the voxels located on a closed band (or “crown”) around the brain, we showed that fMRI data quality can be assessed more effectively. This new feature has been incorporated into MRIQC and fMRIPrep. In addition, a new nuisance regressor has been added to the latter, calculated from timeseries within this new “crown”.