MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites

Quality control of MRI is essential for excluding problematic acquisitions and avoiding bias in subsequent image processing and analysis. Visual inspection is subjective and impractical for large scale datasets. Although automated quality assessments have been demonstrated on single-site datasets, i...

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Vydáno v:PloS one Ročník 12; číslo 9; s. e0184661
Hlavní autoři: Esteban, Oscar, Birman, Daniel, Schaer, Marie, Koyejo, Oluwasanmi O., Poldrack, Russell A., Gorgolewski, Krzysztof J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 25.09.2017
Public Library of Science (PLoS)
Témata:
Artificial intelligence
Autism
Automation
Biology and Life Sciences
Brain - diagnostic imaging
Brain research
Classifiers
Computer and Information Sciences
Computer science
Data acquisition
Datasets
Engineering and Technology
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image processing
Image quality
Information processing
Inspection
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Medical imaging
Medicine and Health Sciences
Methods
Neuroimaging
Neuroimaging - methods
Neurology
NMR
Noise
Nuclear magnetic resonance
Observer Variation
Quality assessment
Quality control
Research and Analysis Methods
Social Sciences
Software
Statistical analysis
Statistical methods
Visual perception
United States
United States > US
California
ISSN:1932-6203, 1932-6203
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Shrnutí:Quality control of MRI is essential for excluding problematic acquisitions and avoiding bias in subsequent image processing and analysis. Visual inspection is subjective and impractical for large scale datasets. Although automated quality assessments have been demonstrated on single-site datasets, it is unclear that solutions can generalize to unseen data acquired at new sites. Here, we introduce the MRI Quality Control tool (MRIQC), a tool for extracting quality measures and fitting a binary (accept/exclude) classifier. Our tool can be run both locally and as a free online service via the OpenNeuro.org portal. The classifier is trained on a publicly available, multi-site dataset (17 sites, N = 1102). We perform model selection evaluating different normalization and feature exclusion approaches aimed at maximizing across-site generalization and estimate an accuracy of 76%±13% on new sites, using leave-one-site-out cross-validation. We confirm that result on a held-out dataset (2 sites, N = 265) also obtaining a 76% accuracy. Even though the performance of the trained classifier is statistically above chance, we show that it is susceptible to site effects and unable to account for artifacts specific to new sites. MRIQC performs with high accuracy in intra-site prediction, but performance on unseen sites leaves space for improvement which might require more labeled data and new approaches to the between-site variability. Overcoming these limitations is crucial for a more objective quality assessment of neuroimaging data, and to enable the analysis of extremely large and multi-site samples.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0184661