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Related Experiment Video

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Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
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Quantitative assessment of structural image quality.

Adon F G Rosen1, David R Roalf1, Kosha Ruparel1

  • 1Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA.

Neuroimage
|December 27, 2017
PubMed
Summary

Objective measures for brain imaging data quality are crucial, especially in developmental studies. This research shows the Euler number accurately detects poor quality T1-weighted brain scans, preventing biased age-related findings.

Keywords:
ArtifactDevelopmentMRIMotionStructural imagingT1

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Area of Science:

  • Neuroimaging
  • Developmental Neuroscience
  • Data Science

Background:

  • Data quality is a critical confounder in brain imaging research, particularly for developmental studies where age correlates with motion and data quality.
  • In-scanner head motion is known to bias structural neuroimaging measures, yet objective quality metrics are often unavailable for T1-weighted volumes.

Purpose of the Study:

  • To identify quantitative data quality measures for T1-weighted brain volumes.
  • To assess the relationship between these quality measures and cortical thickness.
  • To determine how data quality may bias inferences about age-related brain development in youth.

Main Methods:

  • Manual ratings of 1840 T1-weighted brain volumes by three expert raters.
  • Comparison of manual ratings with automated quality measures, including the Preprocessed Connectomes Project's Quality Assurance Protocol (QAP) and FreeSurfer's Euler number.
  • Evaluation of the Euler number's accuracy in identifying unusable images and its correlation with cortical thickness.

Main Results:

  • The Euler number demonstrated consistent correlation with manual quality ratings across multiple datasets.
  • The Euler number accurately identified images rated as unusable by human raters (AUC: 0.98-0.99), outperforming other automated measures.
  • The Euler number showed a significant, regionally specific relationship with cortical thickness, consistent across datasets.
  • Data quality was found to both inflate and obscure associations between brain structure and age during adolescence.

Conclusions:

  • Reliable, automated measures of data quality can be derived directly from T1-weighted brain volumes.
  • Failure to account for data quality in neuroimaging studies, especially those examining brain maturation, can lead to systematic biases in results.
  • The Euler number serves as a robust and accurate metric for assessing T1-weighted brain image quality.