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Crowdsourced MRI quality metrics and expert quality annotations for training of humans and machines

View ORCID ProfileOscar Esteban, View ORCID ProfileRoss W Blair, View ORCID ProfileDylan M Nielson, Jan C Varada, View ORCID ProfileSean Marrett, View ORCID ProfileAdam G Thomas, View ORCID ProfileRussell A Poldrack, View ORCID ProfileKrzysztof J Gorgolewski
doi: https://doi.org/10.1101/420984
Oscar Esteban
1Dept. of Psychology, Stanford University, CA, USA
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Ross W Blair
1Dept. of Psychology, Stanford University, CA, USA
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Dylan M Nielson
2Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD, USA
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Jan C Varada
3Functional MRI Facility, National Institute of Mental Health, Bethesda, MD, USA
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Sean Marrett
3Functional MRI Facility, National Institute of Mental Health, Bethesda, MD, USA
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Adam G Thomas
2Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD, USA
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Russell A Poldrack
1Dept. of Psychology, Stanford University, CA, USA
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Krzysztof J Gorgolewski
1Dept. of Psychology, Stanford University, CA, USA
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  • ORCID record for Krzysztof J Gorgolewski
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Abstract

The neuroimaging community is steering towards increasingly large sample sizes, which are highly heterogeneous because they can only be acquired by multi-site consortia. The visual assessment of every imaging scan is a necessary quality control step, yet arduous and time-consuming. A sizeable body of evidence shows that images of low quality are a source of variability that may be comparable to the effect size under study. We present the MRIQC WebAPI, an open crowdsourced database that collects image quality metrics extracted from MR images and corresponding manual assessments by experts. The database is rapidly growing, and currently contains over 100,000 records of image quality metrics of functional and anatomical MRIs of the human brain, and over 200 expert ratings. The resource is particularly designed for researchers to share image quality metrics and annotations that can readily be reused in training human experts and machine learning algorithms. The ultimate goal of the MRIQC WebAPI is to allow the development of fully automated quality control tools that outperform expert ratings in identifying subpar images.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted September 18, 2018.
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Crowdsourced MRI quality metrics and expert quality annotations for training of humans and machines
Oscar Esteban, Ross W Blair, Dylan M Nielson, Jan C Varada, Sean Marrett, Adam G Thomas, Russell A Poldrack, Krzysztof J Gorgolewski
bioRxiv 420984; doi: https://doi.org/10.1101/420984
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Crowdsourced MRI quality metrics and expert quality annotations for training of humans and machines
Oscar Esteban, Ross W Blair, Dylan M Nielson, Jan C Varada, Sean Marrett, Adam G Thomas, Russell A Poldrack, Krzysztof J Gorgolewski
bioRxiv 420984; doi: https://doi.org/10.1101/420984

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