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Artificial intelligence (AI) can now rapidly quantify thoracic aorta hemodynamics from 3D anatomic scans, offering an alternative to lengthy 4D flow MRI. This fluid physics-informed cycle generative adversarial network (FPI-CycleGAN) achieves high accuracy in under a second.

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Area of Science:

  • Cardiovascular Imaging
  • Medical Artificial Intelligence
  • Biomedical Engineering

Background:

  • Four-dimensional (4D) flow MRI is crucial for assessing thoracic aorta hemodynamics, but its long acquisition times and complex analysis limit clinical use.
  • Hemodynamic measures are vital biomarkers for cardiovascular risk assessment.
  • Developing faster, more accessible methods for quantifying aortic hemodynamics is essential.

Purpose of the Study:

  • To assess the feasibility and accuracy of a generative AI approach, the fluid physics-informed cycle generative adversarial network (FPI-CycleGAN), for quantifying aortic hemodynamics.
  • To determine if FPI-CycleGAN can provide accurate hemodynamic data directly from anatomic input, serving as an alternative to 4D flow MRI.
  • To evaluate the AI's performance in both bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV) populations.

Main Methods:

  • Retrospective analysis of 1765 patients (1242 BAV, 523 TAV) who underwent 4D flow MRI.
  • Training and testing of FPI-CycleGANs using 3D aortic geometry segmentation as input to predict systolic hemodynamics.
  • Comparison of AI-derived hemodynamics (velocity, wall shear stress, valve stenosis classification) against the reference 4D flow MRI standard.

Main Results:

  • FPI-CycleGAN computed hemodynamics in a mean of 0.15 seconds, significantly faster than 4D flow MRI.
  • The AI demonstrated accurate prediction of 3D velocity vector fields with low bias and excellent agreement.
  • Strong correlation (r² = 0.930-0.957) was found for peak velocities and wall shear stress, with accurate classification of aortic valve stenosis in 85.8% of cases.
  • The AI model showed robustness to input data variations and strong performance on an external test set.

Conclusions:

  • A generative AI approach (FPI-CycleGAN) can accurately derive thoracic aorta 3D hemodynamics from anatomic input in under one second.
  • This AI method shows strong agreement with in vivo 4D flow MRI, offering a rapid and potentially more accessible alternative for hemodynamic assessment.
  • The findings suggest a promising role for AI in improving the efficiency and availability of cardiovascular risk assessment through hemodynamic analysis.