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In vitro Assessment of Aortic Regurgitation Using Four-Dimensional Flow Magnetic Resonance Imaging
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Automated Quantification of Simple and Complex Aortic Flow Using 2D Phase Contrast MRI.

Rui Li1,2, Hosamadin S Assadi1,2, Xiaodan Zhao3

  • 1Norwich Medical School, University of East Anglia, Norfolk NR4 7TJ, UK.

Medicina (Kaunas, Lithuania)
|October 26, 2024
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Summary

Fully automated segmentation of aortic root blood flow using artificial intelligence (AI) shows excellent repeatability compared to manual methods. While most flow indices are comparable, flow helicity and retrograde flow require further investigation for bias.

Keywords:
AIaortaflow displacementvalidation

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Area of Science:

  • Cardiovascular imaging
  • Medical artificial intelligence
  • Hemodynamics

Background:

  • Cardiovascular magnetic resonance (CMR) flow assessment is crucial for physiologic parameters but is labor-intensive.
  • Automated segmentation of phase contrast velocity-encoded aortic root planes is needed to improve efficiency.
  • This study evaluates the accuracy and consistency of AI-driven aortic root flow analysis.

Purpose of the Study:

  • To validate fully automated segmentation of the aortic root plane using AI.
  • To assess the repeatability and accuracy of AI-derived flow indices compared to manual segmentation.
  • To identify potential biases in complex flow parameters derived from automated segmentation.

Main Methods:

  • Convolutional neural networks (AI) were developed and trained on a multicentre cohort.
  • Aortic root images from 125 patients were segmented using AI and compared with manual contours.
  • Simple (e.g., forward/backward flow) and complex (e.g., flow displacement) indices were analyzed.

Main Results:

  • AI-derived simple flow indices demonstrated excellent repeatability and minimal bias compared to manual segmentation.
  • Complex flow indices showed good to excellent repeatability, with minor biases in flow displacement angle and retrograde flow.
  • Significant biases were observed for flow displacement angle change and systolic retrograde flow (p < 0.001 and p < 0.05).

Conclusions:

  • Automated aortic root flow quantification is comparable to manual segmentation with good to excellent repeatability.
  • Flow helicity and systolic retrograde flow require further investigation due to significant bias.
  • Overall, the AI method demonstrates clinical repeatability for most flow parameters.