Patient-specific modeling for left ventricular mechanics using data-driven boundary energies
Patient-specific modeling for left ventricular mechanics using data-driven boundary energies
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DOI：
10.1016/j.cma.2016.08.002
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发表时间：
2017-02-01
影响因子：
7.2
通讯作者：
Nordsletten, D.
中科院分区：
文献类型：
--
作者：
Asner, L.;Hadjicharalambous, M.;Nordsletten, D.
在广泛的可用医学数据的支持下，心脏生物力学模型在提高我们对心脏功能的理解以及协助患者诊断和治疗方面表现出了巨大的潜力。开发精确的患者特定模型的关键一步是部署能够将数据集成到模型中的边界条件，以提高模型保真度。此步骤通常受到稀疏或噪声数据的阻碍，如果直接应用这些数据，可能会将非生理力和伪影引入模型中。为了解决这些问题，在本文中，我们提出了新颖的边界条件，旨在通过使用数据导出的边界能量来平衡数据的准确使用与生理边界力和模型结果。引入的技术采用拉格朗日乘子、惩罚方法和基于矩的约束来实现对不同质量和数量的数据的鲁棒性。将所提出的方法与理想化左心室以及体内模型上常用的边界条件进行比较，显示出模型精度的显着提高。边界条件还应用于健康和患病心脏的体内全周期模型，证明了所提出的方法能够在不同的心脏功能范围内重现数据衍生的变形和生理边界力。 (C) 2016 年作者。由 Elsevier B.V. 出版。这是一篇基于 CC BY 许可的开放获取文章。
Supported by the wide range of available medical data available, cardiac biomechanical modeling has exhibited significant potential to improve our understanding of heart function and to assisting in patient diagnosis and treatment. A critical step towards the development of accurate patient-specific models is the deployment of boundary conditions capable of integrating data into the model to enhance model fidelity. This step is often hindered by sparse or noisy data that, if applied directly, can introduce non physiological forces and artifacts into the model. To address these issues, in this paper we propose novel boundary conditions which aim to balance the accurate use of data with physiological boundary forces and model outcomes through the use of data-derived boundary energies. The introduced techniques employ Lagrange multipliers, penalty methods and moment-based constraints to achieve robustness to data of varying quality and quantity. The proposed methods are compared with commonly used boundary conditions over an idealized left ventricle as well as over in vivo models, exhibiting significant improvement in model accuracy. The boundary conditions are also employed in in vivo full-cycle models of healthy and diseased hearts, demonstrating the ability of the proposed approaches to reproduce data-derived deformation and physiological boundary forces over a varied range of cardiac function. (C) 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license.