Non-invasive hemodynamic analysis for aortic regurgitation using
computational fluid dynamics and deep learning
- URL: http://arxiv.org/abs/2111.11660v1
- Date: Tue, 23 Nov 2021 05:19:42 GMT
- Title: Non-invasive hemodynamic analysis for aortic regurgitation using
computational fluid dynamics and deep learning
- Authors: Derek Long, Cameron McMurdo, Edward Ferdian, Charlene Mauger
- Abstract summary: Changes in cardiovascular hemodynamics are closely related to the development of aortic regurgitation (AR)
These metrics can be non-invasively obtained using four-dimensional (4D) flow magnetic resonance imaging (MRI)
However, insufficient resolution often results from limitations in 4D flow MRI and complex AR 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.
- Score: 2.150638298922378
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Changes in cardiovascular hemodynamics are closely related to the development
of aortic regurgitation (AR), a type of valvular heart disease. Pressure
gradients 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 AR 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 AR hemodynamics in a non-invasive
manner.
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