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In vitro Assessment of Aortic Regurgitation Using Four-Dimensional Flow Magnetic Resonance Imaging
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WSSNet: Aortic Wall Shear Stress Estimation Using Deep Learning on 4D Flow MRI.

Edward Ferdian1, David J Dubowitz1, Charlene A Mauger1

  • 1Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.

Frontiers in Cardiovascular Medicine
|February 10, 2022
PubMed
Summary

A new deep learning algorithm, WSSNet, accurately estimates wall shear stress (WSS) from 4D Flow MRI, overcoming limitations of current methods and providing more precise WSS values for cardiovascular research.

Keywords:
4D Flow MRIaortacomputational fluid dynamicsdeep learningwall shear stress (WSS)

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Area of Science:

  • Cardiovascular Imaging
  • Biomedical Engineering
  • Computational Fluid Dynamics

Background:

  • Wall shear stress (WSS) is crucial in vascular remodeling and atherosclerosis.
  • Current 4D Flow MRI techniques underestimate WSS and are limited by spatial resolution.

Purpose of the Study:

  • To develop and validate a deep learning algorithm (WSSNet) for accurate WSS estimation from 4D Flow MRI.
  • To improve the accuracy of WSS measurement in clinical settings.

Main Methods:

  • WSSNet was trained on computational fluid dynamics (CFD) simulations using patient-specific aortic geometries.
  • 3D CFD velocity data were transformed into 2D "velocity sheets" at varying distances from the vessel surface.
  • The algorithm was validated against CFD WSS and noisy synthetic 4D Flow MRI data.

Main Results:

  • WSSNet demonstrated high accuracy against CFD WSS (MAE 0.55 ± 0.60 Pa, R 0.92 ± 0.05).
  • The algorithm performed well on noisy synthetic 4D Flow MRI data (MAE 0.99 ± 0.91 Pa, R 0.79 ± 0.10).
  • Compared to parabolic fitting, WSSNet yielded 2-3x higher WSS values, closer to CFD results (R 0.68 ± 0.12).

Conclusions:

  • WSSNet accurately estimates spatiotemporal WSS from 4D Flow MRI, even with varying image resolutions.
  • This deep learning approach offers a more accurate alternative to existing methods for WSS assessment.
  • The method preserves correct WSS pattern distribution, aiding cardiovascular research and clinical applications.