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Related Experiment Videos

Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM) in big data analysis: U.K. Biobank 18,608

Ivan I Maximov1,2,3, Dennis van der Meer2,4, Ann-Marie G de Lange1,2,5,6

  • 1Department of Psychology, University of Oslo, Oslo, Norway.

Human Brain Mapping
|March 31, 2021
PubMed
Summary

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A new quality control method, YTTRIUM, efficiently identifies poor quality diffusion MRI data and artifacts. This improves the accuracy of machine learning models, such as those used for brain age prediction.

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Reliable brain structural and functional information is crucial for neuroscience.
  • Diffusion magnetic resonance imaging (dMRI) is vital for in vivo brain microstructure analysis in large-scale studies.
  • Automated quality control (QC) is essential for dMRI due to its sensitivity to motion and artifacts.

Purpose of the Study:

  • To develop a robust and efficient QC method for derived diffusion metrics.
  • To address limitations of QC on raw dMRI data, which do not guarantee artifact-free derived metrics.
  • To introduce YTTRIUM, a novel QC method for diffusion scalar metrics.

Main Methods:

  • YTTRIUM utilizes structural similarity to evaluate diffusion map quality and mean diffusion metrics.
Keywords:
DKIDTIU.K. BiobankWMTIYTTRIUMbrain maturationdiffusion QC

Related Experiment Videos

  • The method was applied to U.K. Biobank data (n=18,608) using tract-based spatial statistics.
  • The impact of YTTRIUM on machine learning (ML) based brain age prediction was assessed.

    • Main Results:

    • YTTRIUM demonstrated efficiency in identifying poor quality datasets and artifacts.
    • Application in tract-based spatial statistics revealed associations between age and white matter integrity.
    • Applying YTTRIUM improved the accuracy of ML-based brain age prediction by mitigating outlier effects.

    Conclusions:

    • YTTRIUM is an efficient QC pipeline for diffusion MRI data.
    • The method enhances the reliability of neuroimaging analyses, particularly in large population studies.
    • Improved data quality through YTTRIUM leads to more accurate ML predictions in neuroscience.