Non-invasive hemodynamic analysis for aortic regurgitation using computational fluid dynamics and deep learning

TL;DR

This paper introduces a novel non-invasive method combining computational fluid dynamics and deep learning to enhance 4D flow MRI data, improving the analysis of aortic regurgitation hemodynamics.

Contribution

It develops a deep learning framework trained on synthetic CFD data to generate super-resolution and de-noised 4D flow MRI images for better diagnosis.

Findings

Reduced velocity error in super-resolution images

High structural similarity scores achieved

Successful de-noising of in-vivo flow MRI data

Abstract

Changes in cardiovascular hemodynamics are closely related to the development of aortic regurgitation, a type of valvular heart disease. Metrics derived from blood flows are used to indicate aortic regurgitation onset and evaluate its severity. These metrics can be non-invasively obtained using four-dimensional (4D) flow magnetic resonance imaging (MRI), where accuracy is primarily dependent on spatial resolution. However, insufficient resolution often results from limitations in 4D flow MRI and complex aortic regurgitation hemodynamics. To address this, computational fluid dynamics simulations were transformed into synthetic 4D flow MRI data and used to train a variety of neural networks. These networks generated super resolution, full-field phase images with an upsample factor of 4. Results showed decreased velocity error, high structural similarity scores, and improved learning capabilities from previous work. Further validation was performed on two sets of in-vivo 4D flow MRI data and demonstrated success in de-noising flow images. This approach presents an opportunity to comprehensively analyse aortic regurgitation hemodynamics in a non-invasive manner.