Digging Deeper into the Pervasive Problem of Non-Compliance in MR datasets

COEX 

Large-scale neuroimaging datasets are vital for brain-behavior studies, but the reliability of statistical results depends on its data quality. Therefore, protocol compliance becomes indispensable, emphasizing the need for accurate data acquisition for each subject across sites and scanners. Manual protocol compliance is impractical especially for massive datasets, necessitating an automated approach for minimizing non-compliance.

We have demonstrated the pervasive lack of compliance in large-scale datasets [1] using our open-source tool mrQA, revealing a substantial non-compliance rate of up to 60%, even though the initial exploration focused on a limited subset of parameters.

mrQA can now inspect many more parameters to generate a comprehensive compliance report. Apart from ensuring that all subjects were acquired accurately for each sequence (horizontal audit), mrQA also checks if related sequences acquired within a session are compatible with each other (vertical audit) as shown in Figure 1. With the integration of deeper checks with additional parameters, it becomes apparent that more issues may emerge, emphasizing the need for rigorous monitoring practices. We also explore patterns of non-compliance across scanner vendors, models, and sites such that appropriate strategies can be adopted to minimize such issues at MR imaging centers.