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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2302.10715 |
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| _version_ | 1866917818599997440 |
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| author | Wei, Zhenglun Alan Chen, Guihong Si, Biao Sun, Liqun Seed, Mike Ge, Shuping |
| author_facet | Wei, Zhenglun Alan Chen, Guihong Si, Biao Sun, Liqun Seed, Mike Ge, Shuping |
| contents | Fluid-structure interaction (FSI) modeling, a technique widely used to enhance imaging modalities for adult and pediatric heart diseases, has been underutilized in the context of fetal circulation because of limited data on flow conditions and material properties. Recognizing the significant impact of congenital heart diseases on the fetal aorta, our research aims to address this gap by developing and validating a personalized FSI model for the fetal aorta.
Our approach involved reconstructing the anatomy and flow of the fetal aorta using fetal echocardiography and ultrasound. We developed an innovative iterative method that includes: (i) an automated process for incorporating Windkessel models at outflow boundaries when clinical data is limited because of the resolution constraints of fetal imaging, (ii) an inverse approach to estimate bulk material properties, and (iii) an FSI model for high-fidelity hemodynamic evaluation. This method is efficient, typically converging in fewer than three iterations.
We analyzed four normal fetal aortas with gestational ages ranging from 23.5 to 35.5 weeks to validate our workflow. We compared results with in vivo velocity waveforms across a cardiac cycle at the aortic isthmus. Strong correlations (R>0.95) were observed. Furthermore, our findings suggest that the stiffness of the fetal aorta increases until 30 weeks of gestation and then decreases.
This study marks a first-of-its-kind effort in developing a rigorously validated, personalized flow model for fetal circulation, offering novel insights into fetal aortic development and growth. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_10715 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Personalized Fluid-structure Interaction Modeling Paradigm for Aorta in Human Fetuses Wei, Zhenglun Alan Chen, Guihong Si, Biao Sun, Liqun Seed, Mike Ge, Shuping Medical Physics Fluid-structure interaction (FSI) modeling, a technique widely used to enhance imaging modalities for adult and pediatric heart diseases, has been underutilized in the context of fetal circulation because of limited data on flow conditions and material properties. Recognizing the significant impact of congenital heart diseases on the fetal aorta, our research aims to address this gap by developing and validating a personalized FSI model for the fetal aorta. Our approach involved reconstructing the anatomy and flow of the fetal aorta using fetal echocardiography and ultrasound. We developed an innovative iterative method that includes: (i) an automated process for incorporating Windkessel models at outflow boundaries when clinical data is limited because of the resolution constraints of fetal imaging, (ii) an inverse approach to estimate bulk material properties, and (iii) an FSI model for high-fidelity hemodynamic evaluation. This method is efficient, typically converging in fewer than three iterations. We analyzed four normal fetal aortas with gestational ages ranging from 23.5 to 35.5 weeks to validate our workflow. We compared results with in vivo velocity waveforms across a cardiac cycle at the aortic isthmus. Strong correlations (R>0.95) were observed. Furthermore, our findings suggest that the stiffness of the fetal aorta increases until 30 weeks of gestation and then decreases. This study marks a first-of-its-kind effort in developing a rigorously validated, personalized flow model for fetal circulation, offering novel insights into fetal aortic development and growth. |
| title | A Personalized Fluid-structure Interaction Modeling Paradigm for Aorta in Human Fetuses |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2302.10715 |