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Fig 7 from Computational tools for clinical support: a multi-scale compliant model for haemodynamic simulations in an aortic dissection based on multi-modal imaging data.

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DataCite Commons2020-10-15 更新2024-07-25 收录
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https://rs.figshare.com/articles/dataset/Fig_7_from_Computational_tools_for_clinical_support_a_multi-scale_compliant_model_for_haemodynamic_simulations_in_an_aortic_dissection_based_on_multi-modal_imaging_data/5555149
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资源简介:
Aortic dissection (AD) is a vascular condition with high morbidity and mortality rates. Computational fluid dynamics (CFD) can provide insight into the progression of AD and aid clinical decisions; however, oversimplified modelling assumptions and high computational cost compromise the accuracy of the information and impede clinical translation. To overcome these limitations, a patient-specific CFD multi-scale approach coupled to Windkessel boundary conditions and accounting for wall compliance was developed and used to study an AD patient. A new moving boundary algorithm was implemented to capture wall displacement and a rich <i>in vivo</i> clinical dataset was used to tune model parameters and for validation. Comparisons between <i>in silico</i> and <i>in vivo</i> data showed that this approach successfully captures flow and pressure waves for the patient-specific AD and is able to predict the pressure in the false lumen (FL), a critical variable for the clinical management of the condition. Results showed regions of low and oscillatory wall shear stress which, together with higher diastolic pressures predicted in the FL, may indicate risk of expansion. This study, at the interface of engineering and medicine, demonstrates a relatively simple and computationally efficient approach to account for arterial deformation and wave propagation phenomena in a three-dimensional model of AD, representing a step forward in the use of CFD as potential tool for AD management and clinical support.

主动脉夹层(Aortic dissection, AD)是一类发病率与死亡率均较高的血管疾病。计算流体动力学(Computational fluid dynamics, CFD)可为主动脉夹层的病情进展提供机制洞察,并辅助临床决策;然而,过度简化的建模假设与高昂的计算成本,既会降低所得信息的准确性,也阻碍了其临床转化。为克服上述局限,本研究开发了一种耦合Windkessel边界条件且考量血管壁顺应性的患者特异性CFD多尺度方法,并将其应用于一例主动脉夹层患者的研究。研究中实现了一种全新的运动边界算法以捕捉血管壁位移,并采用丰富的体内(in vivo)临床数据集对模型参数进行校准与验证。虚拟(in silico)与体内(in vivo)数据的对比结果表明,该方法可成功捕捉针对该患者特异性主动脉夹层的血流与压力波,且能够预测假腔(false lumen, FL)内的压力——这是该疾病临床管理中的关键变量。研究结果显示存在低振荡壁面切应力区域,结合假腔内预测得到的更高舒张压,或可提示夹层扩张风险。本研究横跨工程与医学交叉领域,证实了一种相对简洁且计算高效的方法,可在主动脉夹层的三维模型中纳入动脉变形与波传播现象,为将CFD作为主动脉夹层管理与临床辅助的潜在工具迈出了重要一步。
提供机构:
The Royal Society
创建时间:
2017-10-31
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