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

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