Abstract #3416

MRIQC Web-API: Crowdsourcing image quality metrics and expert quality ratings of structural and functional MRI

Oscar Esteban¹, Ross W Blair¹, Dylan M Nielson², Jan C Varada³, Sean Marrett³, Adam G Thomas², Russell A Poldrack¹, and Krzysztof J Gorgolewski¹

¹Dept. of Psychology, Stanford University, Stanford, CA, United States, ²Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD, United States, ³Functional Magnetic Resonance Imaging Facility, National Institute of Mental Health, Bethesda, MD, United States

The MRIQC Web-API is a resource for scientists to train new automatic quality classifiers. The MRIQC Web-API has collected more than 30K sets of image quality measures automatically extracted from BOLD and T1-weighted scans using MRIQC. MRIQC is an automated MRI Quality Control tool, and here we present an extension to crowdsource these quality metrics along with anonymized metadata and manual quality ratings. This new resource will allow a better understanding of the normative values and distributions of these quality metrics, help determine the relationships between image quality and metadata such as acquisition parameters and finally, provide a cost-effective, easy way to annotate the quality of a large number of cross-site MR scans.

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