Non-invasive hemodynamic analysis for aortic regurgitation using computational fluid dynamics and deep learning. (arXiv:2111.11660v2 [cs.CV] UPDATED)

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 …

analysis arxiv computational cv deep learning dynamics fluid dynamics learning