School of Engineering, National University of Ireland Galway, Galway, Ireland.
Amsterdam University Medical Centers, Amsterdam, The Netherlands.
J Biomech. 2022 Mar;133:110896. doi: 10.1016/j.jbiomech.2021.110896. Epub 2022 Jan 7.
Development of in-silico models of patient-specific cerebral artery networks presents several significant technical challenges: (i) The resolution and smoothness of medical CT images are much lower than the required element/cell length for FEA/CFD/FSI models; (ii) contact between vessels, and indeed self contact of high tortuosity vessel segments are not clearly identifiable from medical CT images. Commercial model construction software does not provide customised solutions for such technical challenges, with the result that accurate, efficient and automated development of patient-specific models of the cerebral vessels is not facilitated. This paper presents the development of a customised and highly automated platform for the generation of high resolution patient-specific FEA/CFD/FSI models from clinical images. This platform is used to perform the first fluid-structure-interaction patient-specific analysis of blood flow and artery deformation of an occluded cerebral vessel. Results demonstrate that in addition to flow disruption, clot occlusion significantly alters the geometry and strain distribution in the vessel network, with the blocked M2 segment undergoing axial elongation. The new computational approach presented in this study can be further developed as a clinical diagnostic tool and as a platform for thrombectomy device design.
(i) 医学 CT 图像的分辨率和光滑度比 FEA/CFD/FSI 模型所需的元素/细胞长度低得多；(ii) 血管之间的接触，甚至是高迂曲血管段的自接触，从医学 CT 图像中无法明确识别。商业模型构建软件并未针对这些技术挑战提供定制解决方案，因此无法方便地实现准确、高效和自动化的特定患者脑血管模型的开发。本文提出了一种定制化和高度自动化的平台，用于从临床图像生成高分辨率的特定患者 FEA/CFD/FSI 模型。该平台用于对阻塞性脑血管的血流和动脉变形进行首次基于流体-结构相互作用的特定患者分析。结果表明，除了血流中断外，血栓阻塞还会显著改变血管网络的几何形状和应变分布，阻塞的 M2 段发生轴向伸长。本研究中提出的新计算方法可以进一步开发为临床诊断工具和取栓装置设计的平台。
