Virtual stenting with simplex mesh and mechanical contact analysis for real-time planning of thoracic endovascular aortic repair
Virtual stenting with simplex mesh and mechanical contact analysis for real-time planning of thoracic endovascular aortic repair
复制标题
使用单纯网片和机械接触分析进行虚拟支架植入，用于胸主动脉腔内修复的实时规划
DOI：
10.7150/thno.28944
复制
发表时间：
2018-01-01
期刊：
影响因子：
12.4
通讯作者：
Tang, Xiaoying
中科院分区：
文献类型：
--
作者：
Chen, Duanduan;Wei, Jianyong;Tang, Xiaoying
在主动脉腔内修复术中，支架血管重塑的预测为治疗计划和风险评估提供了依据;然而，目前还没有高度准确和有效的方法来定量模拟支架血管。本研究开发了一种快速虚拟支架植入算法来模拟支架诱导的主动脉重构，以辅助实时胸主动脉腔内修复术计划。方法：基于单纯形变形网格和机械接触分析，建立虚拟支架植入算法。机械接触分析的关键参数来自主动脉组织（n=40）和常用覆膜支架（n=6）的机械试验。采用遗传算法选择加权参数。使用B型主动脉夹层病例（n=66）的治疗前和治疗后计算机断层扫描血管造影数据集对算法进行测试和验证。结果如下：该算法可有效模拟支架诱导的主动脉变形（单处理器上的平均计算时间：13.78± 2.80 s），在形态学（曲率差异：1.57±0.57%;横截面积差异：4.11±0.85%）和血流动力学（壁面剪切应力衍生参数的相似性：90.16-90.94%）水平上具有准确性。远端支架诱导新进入病例的支架诱导壁变形高于（p<0.05）成功治疗病例，并且不同支架组之间的变形无显著差异。此外，高支架诱导壁变形区域和新进入部位重叠，表明壁变形可用于评价器械诱导并发症的风险。结论：新算法通过管腔变形跟踪提供了覆膜支架展开的快速实时和准确预测，从而可能为个性化支架植入计划提供信息并改善主动脉腔内修复术结局。需要进行大型多中心研究来扩展算法验证，并确定特定并发症风险分层的应力诱导壁变形截止值。
In aortic endovascular repair, the prediction of stented vessel remodeling informs treatment plans and risk evaluation; however, there are no highly accurate and efficient methods to quantitatively simulate stented vessels. This study developed a fast virtual stenting algorithm to simulate stent-induced aortic remodeling to assist in real-time thoracic endovascular aortic repair planning. Methods: The virtual stenting algorithm was established based on simplex deformable mesh and mechanical contact analysis. The key parameters of the mechanical contact analysis were derived from mechanical tests on aortic tissue (n=40) and commonly used stent-grafts (n=6). Genetic algorithm was applied to select weighting parameters. Testing and validation of the algorithm were performed using pre- and post-treatment computed tomography angiography datasets of type-B aortic dissection cases (n=66). Results: The algorithm was efficient in simulating stent-induced aortic deformation (mean computing time on a single processor: 13.78±2.80s) and accurate at the morphological (curvature difference: 1.57±0.57%; cross-sectional area difference: 4.11±0.85%) and hemodynamic (similarity of wall shear stress-derived parameters: 90.16-90.94%) levels. Stent-induced wall deformation was higher (p<0.05) in distal stent-induced new entry cases than in successfully treated cases, and this deformation did not differ significantly among the different stent groups. Additionally, the high stent-induced wall deformation regions and the new-entry sites overlapped, indicating the usefulness of wall deformation to evaluate the risks of device-induced complications. Conclusion: The novel algorithm provided fast real-time and accurate predictions of stent-graft deployment with luminal deformation tracking, thereby potentially informing individualized stenting planning and improving endovascular aortic repair outcomes. Large, multicenter studies are warranted to extend the algorithm validation and determine stress-induced wall deformation cutoff values for the risk stratification of particular complications.