Super-resolution 4D flow MRI to quantify aortic regurgitation using computational fluid dynamics and deep learning.

ABSTRACT: Changes in cardiovascular hemodynamics are closely related to the development of aortic regurgitation (AR), a type of valvular heart disease. Metrics derived from blood flows are used to indicate AR 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 de

SUBMITTER: Long D 

PROVIDER: S-EPMC10220149 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature