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An efficient magnetic resonance image data quality screening dashboard.

Evan D H Gates1,2, Adrian Celaya1, Dima Suki3

  • 1Department of Imaging Physics, MD Anderson Cancer Center, The University of Texas, Houston, Texas, USA.

Journal of Applied Clinical Medical Physics
|February 11, 2022
PubMed
Summary
This summary is machine-generated.

A new imaging informatics dashboard significantly speeds up the review of processed magnetic resonance (MR) imaging data. This tool enhances data quality control for artificial intelligence applications by enabling faster, more accurate assessments.

Keywords:
MRIdashboarddata curationimaging informatics

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Area of Science:

  • Medical imaging informatics
  • Artificial intelligence in healthcare
  • Data quality assurance

Background:

  • High-quality datasets are crucial for training artificial intelligence (AI) models.
  • Manual review of medical images and annotations is time-consuming and labor-intensive.
  • Existing quality control tools often lack capabilities for processed imaging data.

Purpose of the Study:

  • To develop an imaging informatics dashboard for efficient review of processed magnetic resonance (MR) imaging data.
  • To demonstrate the dashboard's effectiveness in large-scale data quality assessment.
  • To improve the speed and accuracy of data curation for AI applications.

Main Methods:

  • Developed a custom R Shiny dashboard for visualizing imaging studies and annotations.
  • Implemented a graphical user interface for structured data review and results tabulation.
  • Evaluated the dashboard on 1380 institutional MR imaging studies and 285 MICCAI BraTS challenge studies.

Main Results:

  • The dashboard enabled review at an average rate of 100 studies/hour, 10x faster than existing viewers.
  • 86% of institutional studies and 85% of BraTS studies were deemed acceptable quality.
  • Identified common failure modes including tumor segmentation (9.6%) and image registration (4.6%); improved segmentation accuracy correlated with dashboard review quality.

Conclusions:

  • The developed dashboard is a fast and effective tool for reviewing complex processed MR imaging datasets.
  • The tool facilitates improved data quality control for AI-driven medical imaging research.
  • The dashboard is freely available for download, promoting wider adoption and data quality improvement.