Pham Jonathan, Kong Fanwei, James Doug L, Feinstein Jeffrey A, Marsden Alison L
Department of Mechanical Engineering, Stanford University, Stanford, CA, USA.
Department of Pediatrics, Stanford University, Stanford, CA, USA.
Cardiovasc Eng Technol. 2024 Dec;15(6):760-774. doi: 10.1007/s13239-024-00752-z. Epub 2024 Oct 1.
Angioplasty with stent placement is a widely used treatment strategy for patients with stenotic blood vessels. However, it is often challenging to predict the outcomes of this procedure for individual patients. Image-based computational fluid dynamics (CFD) is a powerful technique for making these predictions. To perform CFD analysis of a stented vessel, a virtual model of the vessel must first be created. This model is typically made by manipulating two-dimensional contours of the vessel in its pre-stent state to reflect its post-stent shape. However, improper contour-editing can cause invalid geometric artifacts in the resulting mesh that then distort the subsequent CFD predictions. To address this limitation, we have developed a novel shape-editing method that deforms surface meshes of stenosed vessels to create stented models.
Our method uses physics-based simulations via Extended Position Based Dynamics to guide these deformations. We embed an inflating stent inside a vessel and apply collision-generated forces to deform the vessel and expand its cross-section.
We demonstrate that this technique is feasible and applicable for a wide range of vascular anatomies, while yielding clinically compatible results. We also illustrate the ability to parametrically vary the stented shape and create models allowing CFD analyses.
Our stenting method will help clinicians predict the hemodynamic results of stenting interventions and adapt treatments to achieve target outcomes for patients. It will also enable generation of synthetic data for data-intensive applications, such as machine learning, to support cardiovascular research endeavors.
血管成形术加支架置入是治疗血管狭窄患者广泛采用的治疗策略。然而，预测个体患者该手术的结果往往具有挑战性。基于图像的计算流体动力学（CFD）是进行这些预测的有力技术。要对置入支架的血管进行CFD分析，必须首先创建血管的虚拟模型。该模型通常通过操纵血管在置入支架前状态的二维轮廓来反映其置入支架后的形状。然而，不当的轮廓编辑会在生成的网格中导致无效的几何伪影，进而扭曲后续的CFD预测。为解决这一局限性，我们开发了一种新颖的形状编辑方法，该方法可使狭窄血管的表面网格变形以创建置入支架的模型。
我们的方法通过扩展基于位置的动力学使用基于物理的模拟来指导这些变形。我们将一个膨胀的支架嵌入血管内，并施加碰撞产生的力使血管变形并扩大其横截面。
我们证明了该技术是可行的，适用于广泛的血管解剖结构，同时产生临床兼容的结果。我们还展示了参数化改变置入支架形状并创建允许进行CFD分析的模型的能力。
我们的支架置入方法将帮助临床医生预测支架置入干预的血流动力学结果，并调整治疗以实现患者的目标结果。它还将能够为数据密集型应用（如机器学习）生成合成数据，以支持心血管研究工作。
