Center for Cardiovascular Simulation, Institute for Computational Engineering and Sciences, Department of Biomedical Engineering, The University of Texas at Austin, 201 East 24th St, Stop C0200, Austin, Texas, 78712-1229, USA.
Gorman Cardiovascular Research Group, Smilow Center for Translational Research, 3400 Civic Center Blvd - Building 421 11th Floor, Room 112, Philadelphia, PA, 19104-5156, USA.
Biomech Model Mechanobiol. 2018 Feb;17(1):31-53. doi: 10.1007/s10237-017-0943-1. Epub 2017 Aug 31.
Knowledge of the complete three-dimensional (3D) mechanical behavior of soft tissues is essential in understanding their pathophysiology and in developing novel therapies. Despite significant progress made in experimentation and modeling, a complete approach for the full characterization of soft tissue 3D behavior remains elusive. A major challenge is the complex architecture of soft tissues, such as myocardium, which endows them with strongly anisotropic and heterogeneous mechanical properties. Available experimental approaches for quantifying the 3D mechanical behavior of myocardium are limited to preselected planar biaxial and 3D cuboidal shear tests. These approaches fall short in pursuing a model-driven approach that operates over the full kinematic space. To address these limitations, we took the following approach. First, based on a kinematical analysis and using a given strain energy density function (SEDF), we obtained an optimal set of displacement paths based on the full 3D deformation gradient tensor. We then applied this optimal set to obtain novel experimental data from a 1-cm cube of post-infarcted left ventricular myocardium. Next, we developed an inverse finite element (FE) simulation of the experimental configuration embedded in a parameter optimization scheme for estimation of the SEDF parameters. Notable features of this approach include: (i) enhanced determinability and predictive capability of the estimated parameters following an optimal design of experiments, (ii) accurate simulation of the experimental setup and transmural variation of local fiber directions in the FE environment, and (iii) application of all displacement paths to a single specimen to minimize testing time so that tissue viability could be maintained. Our results indicated that, in contrast to the common approach of conducting preselected tests and choosing an SEDF a posteriori, the optimal design of experiments, integrated with a chosen SEDF and full 3D kinematics, leads to a more robust characterization of the mechanical behavior of myocardium and higher predictive capabilities of the SEDF. The methodology proposed and demonstrated herein will ultimately provide a means to reliably predict tissue-level behaviors, thus facilitating organ-level simulations for efficient diagnosis and evaluation of potential treatments. While applied to myocardium, such developments are also applicable to characterization of other types of soft tissues.
了解软组织完整的三维（3D）机械行为对于理解其病理生理学和开发新疗法至关重要。尽管在实验和建模方面取得了重大进展，但仍难以实现全面表征软组织 3D 行为的完整方法。一个主要挑战是软组织（如心肌）的复杂结构，这使它们具有强烈各向异性和不均匀的机械性能。用于量化心肌 3D 机械行为的现有实验方法仅限于预选的平面双轴和 3D 立方剪切试验。这些方法在追求全运动学空间的模型驱动方法方面存在不足。为了解决这些限制，我们采取了以下方法。首先，基于运动学分析并使用给定的应变能密度函数（SEDF），我们基于完整的 3D 变形梯度张量获得了一组基于位移的最佳路径。然后，我们应用该最佳集从梗死的左心室心肌 1 立方厘米的立方体获得新的实验数据。接下来，我们在参数优化方案中嵌入了实验配置的逆有限元（FE）模拟，用于估计 SEDF 参数。该方法的显著特点包括：（i）通过优化实验设计增强了参数的可确定性和预测能力，（ii）在 FE 环境中准确模拟实验设置和局部纤维方向的跨壁变化，以及（iii）将所有位移路径应用于单个样本，以最大限度地减少测试时间，从而保持组织活力。我们的结果表明，与进行预选测试和选择 SEDF 后验的常见方法相比，优化的实验设计、与选定的 SEDF 和完整的 3D 运动学相结合，可以更有效地表征心肌的机械行为，并提高 SEDF 的预测能力。本文提出和演示的方法最终将提供一种可靠预测组织级行为的方法，从而促进器官级模拟，以有效地诊断和评估潜在的治疗方法。虽然应用于心肌，但这种发展也适用于其他类型软组织的特性描述。
