Literature DB >> 33469847

Proper Orthogonal Decomposition Analysis of the Flow Downstream of a Dysfunctional Bileaflet Mechanical Aortic Valve.

Ahmed Darwish1,2, Giuseppe Di Labbio3,4, Wael Saleh3,5, Lyes Kadem3.   

Abstract

PURPOSE: Aortic valve replacement remains the only viable solution for symptomatic patients with severe aortic valve stenosis. Despite their improved design and long history of successful operation, bileaflet mechanical heart valves are still associated with post-operative complications leading to valve dysfunction. Thus, the flow dynamics can be highly disturbed downstream of the dysfunctional valve.
METHODS: In this in vitro study, the flow dynamics downstream of healthy and dysfunctional bileaflet mechanical heart valves have been investigated using particle image velocimetry measurements. Proper orthogonal decomposition of the velocity field has been performed in order to explore the coherent flow features in the ascending aorta in the presence of a dysfunctional bileaflet mechanical heart valve.
RESULTS: The ability of proper orthogonal decomposition derived metrics to differentiate between heathy and dysfunctional cases is reported. Moreover, reduced-order modeling using proper orthogonal decomposition is thoroughly investigated not only for the velocity field but also for higher order flow characteristics such as time average wall shear stress, oscillatory shear index and viscous energy dissipation.
CONCLUSION: Considering these results, proper orthogonal decomposition can provide a rapid binary classifier to evaluate if the bileaflet mechanical valve deviates from its normal operating conditions. Moreover, the study shows that the size of the reduced-order model depends on which flow parameter is required to be reconstructed.

Entities:  

Keywords:  Bileaflet mechanical heart valve; Flow dynamics; Proper orthogonal decomposition; Reduced-order modeling; Valve dysfunction

Year:  2021        PMID: 33469847     DOI: 10.1007/s13239-021-00519-w

Source DB:  PubMed          Journal:  Cardiovasc Eng Technol        ISSN: 1869-408X            Impact factor:   2.495


  3 in total

Review 1.  Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond.

Authors:  Amirhossein Arzani; Jian-Xun Wang; Michael S Sacks; Shawn C Shadden
Journal:  Ann Biomed Eng       Date:  2022-04-20       Impact factor: 3.934

Review 2.  Body Acoustics for the Non-Invasive Diagnosis of Medical Conditions.

Authors:  Jadyn Cook; Muneebah Umar; Fardin Khalili; Amirtahà Taebi
Journal:  Bioengineering (Basel)       Date:  2022-04-01

3.  Spectral Decomposition of the Flow and Characterization of the Sound Signals through Stenoses with Different Levels of Severity.

Authors:  Fardin Khalili; Peshala T Gamage; Amirtahà Taebi; Mark E Johnson; Randal B Roberts; John Mitchell
Journal:  Bioengineering (Basel)       Date:  2021-03-19
  3 in total

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