Unraveling aortic hemodynamics using fluid structure interaction: biomechanical insights into bicuspid aortic valve dynamics with multiple aortic lesions: Unraveling aortic hemodynamics using fluid structure...: V. Govindarajan et al.
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| Title: | Unraveling aortic hemodynamics using fluid structure interaction: biomechanical insights into bicuspid aortic valve dynamics with multiple aortic lesions: Unraveling aortic hemodynamics using fluid structure...: V. Govindarajan et al. |
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| Authors: | Govindarajan, Vijay1,2,3 (AUTHOR) Vijay.Govindarajan@uth.tmc.edu, Wanna, Charles1 (AUTHOR), Johnson, Nils P.1 (AUTHOR), Kolanjiyil, Arun V.4 (AUTHOR), Kim, Hyunggun5 (AUTHOR), Kitkungvan, Danai1 (AUTHOR), McPherson, David M.1 (AUTHOR), Grande-Allen, Jane6 (AUTHOR), Chandran, Krishnan B.1,7 (AUTHOR), Estrera, Antony1 (AUTHOR), Ramzy, Danny1 (AUTHOR), Prakash, Siddharth1 (AUTHOR) |
| Source: | Biomechanics & Modeling in Mechanobiology. Feb2025, Vol. 24 Issue 1, p17-27. 11p. |
| Subjects: | Mitral valve, Medical sciences, Congenital heart disease, Turner's syndrome, Aorta |
| Abstract: | Aortic lesions, exemplified by bicuspid aortic valves (BAVs), can complicate congenital heart defects, particularly in Turner syndrome patients. The combination of BAV, dilated ascending aorta, and an elongated aortic arch presents complex hemodynamics, requiring detailed analysis for tailored treatment strategies. While current clinical decision-making relies on imaging modalities offering limited biomechanical insights, integrating high-performance computing and fluid–structure interaction algorithms with patient data enables comprehensive evaluation of diseased anatomy and planned intervention. In this study, a patient-specific workflow was utilized to biomechanically assess a Turner syndrome patient's BAV, dilated ascending aorta, and elongated arch. Results showed significant improvements in valve function (effective orifice area, EOA increased approximately twofold) and reduction in valve stress (~ 1.8-fold) following virtual commissurotomy, leading to enhanced flow dynamics and decreased viscous dissipation (~ twofold) particularly in the ascending aorta. However, increased viscous dissipation in the distal transverse aortic arch offset its local reduction in the AAo post-intervention, emphasizing the elongated arch's role in aortic hemodynamics. Our findings highlight the importance of comprehensive biomechanical evaluation and integrating patient-specific modeling with conventional imaging techniques for improved disease assessment, risk stratification, and treatment planning, ultimately enhancing patient outcomes. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Aortic lesions, exemplified by bicuspid aortic valves (BAVs), can complicate congenital heart defects, particularly in Turner syndrome patients. The combination of BAV, dilated ascending aorta, and an elongated aortic arch presents complex hemodynamics, requiring detailed analysis for tailored treatment strategies. While current clinical decision-making relies on imaging modalities offering limited biomechanical insights, integrating high-performance computing and fluid–structure interaction algorithms with patient data enables comprehensive evaluation of diseased anatomy and planned intervention. In this study, a patient-specific workflow was utilized to biomechanically assess a Turner syndrome patient's BAV, dilated ascending aorta, and elongated arch. Results showed significant improvements in valve function (effective orifice area, EOA increased approximately twofold) and reduction in valve stress (~ 1.8-fold) following virtual commissurotomy, leading to enhanced flow dynamics and decreased viscous dissipation (~ twofold) particularly in the ascending aorta. However, increased viscous dissipation in the distal transverse aortic arch offset its local reduction in the AAo post-intervention, emphasizing the elongated arch's role in aortic hemodynamics. Our findings highlight the importance of comprehensive biomechanical evaluation and integrating patient-specific modeling with conventional imaging techniques for improved disease assessment, risk stratification, and treatment planning, ultimately enhancing patient outcomes. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 16177959 |
| DOI: | 10.1007/s10237-024-01892-w |
