Super-resolution 4D flow MRI to quantify aortic regurgitation using computational fluid dynamics and deep learning
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10554_2023_Article_2815.pdf
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Checksum (MD5)
e1ceaadeb5c3d05aa388ebcb427fb073
Author(s)
Long, Derek
McMurdo, Cameron
Ferdian, Edward
Mauger, Charlène A.
Marlevi, David
Nash, Martyn P.
Young, Alistair A.
Date Issued
February 23, 2023
Publisher
Springer Netherlands
Citation
Long, Derek, McMurdo, Cameron, Ferdian, Edward, Mauger, Charlène A., Marlevi, David et al. 2023. "Super-resolution 4D flow MRI to quantify aortic regurgitation using computational fluid dynamics and deep learning."
Version
Final published version
Abstract
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 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.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
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DOI of Published Version
https://doi.org/10.1007/s10554-023-02815-z
