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基于CTA的个体化脑动脉瘤的流固耦合分析及其临床应用

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  摘  要: 探讨基于CTA的个体化脑动脉瘤的流固耦合分析,为临床应用提供帮助。利用真实患者的脑动脉瘤DICOM格式影像检查数据,利用MIMICS软件进行三维重建血流模型、应用3-matic软件进行修复、光顺,应用ANSYS  ICEM  CFD软件生成流体的有限元模型,利用HYPERWORKS SimLab软件基于流体模型构建动脉壁有限元模型,使用ANSYS  FLUENT+Transient Structural进行双向流固耦合计算一个心动周期的动脉瘤血流动力学参数。本文有限元模型的构建方法可有效的分析动脉瘤的血流动力学参数,为临床提供科学的理论指导。
  关键词: 脑动脉瘤;血流动力学;计算流体力学分析
  中图分类号: TP319    文献标识码: A    DOI:10.3969/j.issn.1003-6970.2020.01.020
  本文著录格式:陈广新,赵东良,郭金兴,等. 基于CTA的个体化脑动脉瘤的流固耦合分析及其临床应用[J]. 软件,2020,41(01):97101
  【Abstract】: The fluid-solid coupling analysis of individualized cerebral aneurysms based on CTA was discussed to provide help for clinical application. Using DICOM format image examination data of cerebral aneurysms of real patients, three-dimensional blood flow model reconstruction is performed by MIMICS software, repair and smoothing are performed by 3-matic software, finite element model of fluid is generated by ANSYS ICEM CFD software, finite element model of arterial wall is constructed by HYPERWORKS SimLab software based on fluid model, and bidirectional fluid-solid coupling is performed by Ansys Fluent+Transient Structure to calculate hemodynamic parameters of aneurysm in one cardiac cycle. The construction method of finite element model in this paper can effectively analyze the hemodynamic parameters of aneurysm and provide scientific theoretical guidance for clinic.
  【Key words】: Cerebral aneurysm; Hemodynamics; Computational fluid dynamics analysis
  0  引言
  颅内动脉瘤(intracranial aneurysms,IA)是一种严重危害健康的脑血管疾病,普通人群发病率约为3.2%[1]。IA一旦破裂,致残率及致死率非常高[2]。随着医学影像诊断技术的提高,IA大多可准确诊断[3]。计算流体力学(computational fluid dynamics,CFD)对血流动力学的研究提供了可以借鉴的方法,大大的拓宽了血流动力学的研究途径。血流动力学因素包括壁面压力、血流速度、壁面剪切应力等被认为是影响动脉瘤的发生发展的重要因素。应用计算流体力学对动脉瘤进行血流动力学研究,具有可控性好、稳定性高、计算准确等优势。国内外学者已经基于CFD方法对血流动力学进行了大量的研究[4,5]。以往的研究偏重于流体力学分析,对动脉瘤进行流固耦合分析较少。本文基于个体化动脉瘤CTA影像数据,分别构建血流、动脉壁的有限元模型,采用双向流固耦合计算,获得一个心动周期的血流动力参数,分析动脉瘤的血流动力学特点,为临床的研究提供借鉴。
  1  材料与方法
  1.1  图像采集
  采集牡丹江医学院附属红旗医院一名动脉瘤患者未破裂脑动脉瘤影像数据,患者知情同意,经医院伦理委员会批准。使用日本东芝Aquilion 64层螺旋CT行头部CTA检查,检查数据以DICOM格式输出。
  1.2  三维模型构建
  使用MIMICS 21.0软件手动导入采集的动脉瘤DICOM格式文件。采用阈值分割、区域增长等算法,计算三维模型,获得动脉瘤3D模型,并经手动分割,保留载瘤动脉。将获得的3D动脉瘤模型以stl格式导入正向工程软件3-matic进行光顺、表面网格优化,最终获得包括动脉瘤的血流3D模型(图1)。动脉壁的模型须依此生成。
  1.3  有限元网格生成
  动脉壁网格:基于动脉瘤stl格式直接生成动脉壁网格。将.stl格式的动脉瘤模型导入SimLab软件(美国Altair公司有限元网格划分软件)中,生成三层的棱形网格结构,壁厚0.2 mm(图2)。
  動脉瘤网格:应用ANSYS ICEM CFD(美国ANSYS公司流体网格划分软件)软件划分动脉瘤.stl格式模型。流体采用四面体类型、为保证计算精度边界层5层加密(图3)。   1.4  初始條件与边界条件设置
  假设血流为不可压缩、层流的牛顿流体,忽略重力的影响,血液密度为1050 kg/m3,粘度为0.0035 Pa·s,动脉瘤壁为线弹性材料,壁厚为0.2 mm,密度为1150kg/m3,弹性模量和泊松比分别为50kPa和0.45[9,10],血管进出口采用固定支撑,血管壁无血流渗透且与血流之间无滑动。流体采用速度入口,入口血流速度曲线如图4所示,出口压力为0 [6-12]。
  2  结果
  采用流固耦合的研究方法进行数值模拟计算,
  分别获得模型的血流速度(V)、壁总压力(P)、总变形量(total deformation,TD)数据。
  2.1  动脉瘤壁变形(TD)
  动脉瘤壁面变形与动脉瘤增生关系密切,动脉瘤壁面变形过大,则易导致动脉瘤破裂出血。图5为脉动周期中0.2 s、0.4 s、0.6 s时刻的动脉瘤壁变形。由图可见,动脉瘤壁面的变形达到整个动脉壁区域的最大值。在心动周期内,动脉瘤变形云图的梯度分布仍无明显变化,但形变的最大值呈逐渐减小的趋势。时刻动脉瘤总体的变形量大幅度减小,其中动脉瘤上的变形梯度有所改变,最大变形位置向瘤顶中心偏移,梯度也从瘤颈至瘤顶呈现从下至上递增[13-15]。
  2.2  动脉瘤血流速度流线图
  图6为动脉瘤典型时刻(0.1 s、0.2 s、0.4 s)血流速度流线图,从图中可以看出,血液沿血管轴向速度递减,血管中心线附近血流速度最快。0.1-0.2秒载瘤动脉的血流速度不断增大,呈层流状态。瘤内的血流形成了涡流。在血流速度上升期内,瘤内涡流逐步增强,在0.2 s-0.4 s血流速度下降期,涡流又逐渐减小。由于血液从中间血管向两侧血管流动,中间血管流过来的血液首先对动脉瘤的瘤颈处造成冲击,然后进入动脉瘤腔内[17-20]。
  
  2.3  壁面压力力云图
  图7为动脉瘤典型时刻(0.1 s、0.2 s、0.4 s)壁面压力。由图可以看出,壁面压力随着血流速度的舒张、收缩期发生变化。在血流速度达到峰值时,壁面压力也随之达到峰值。壁面压力的大小决定着动脉瘤的破裂机率。壁面压力存在负值现象,此种情形即所谓的“负压”[21-23]。
  3  讨论
  本文基于个体化CTA影像数据构建脑动脉瘤流固耦合模型,并就动脉瘤的发生、发展及破裂的血流动力学因素进行了分析。在血流、动脉壁模型构建中,采用基于动脉血流的模型直接构建三层的血管壁网格结构,此种方式构建的准确性高,并降低网格划分的难度,提高了有限元计算的效率。另一方面,基于流固耦合计算的脑动脉瘤数值模拟分析,对观察、预测脑动脉瘤的发生、发展机制,提供真实、可靠的依据。
  参考文献
  [1] Paolo Di Achille, Jay D. Humphrey. TowardLarge-Scal Computational Fluid-Solid-Growth Models of Intracranial Aneurysms[J]. Yale J Biol Med, 2012, 85(2): 217-228.
  [2] Fuyu Wang, Bainan Xu, Zhenghui Sun, Chen Wu, Xiaojun Zhang. Wall shear stress in intracranial aneurysms and adjacent arteries[J]. Neural Regen Res, 2013, 15; 8(11): 1007-1015.
  [3] J.D. Humphrey. Coupling hemodynamics with vascular wall mechanics and mechanobiology to understand intracranial aneurysms[J]. Int J Comut Fluid Dyn, 2009, 23(8): 569-581.
  [4] Benjamin Owen, Nicholas Bojdo, Andrey Jivkov, Bernard Keavney, Alistair Revell. Structural modelling of the cardiovascular system[J]. Biomech Model Mechanobiol, 2018, 17(5): 1217-1242.
  [5] Xu Bai-Nan, Wang Fu-Yu, Liu Lei, Zhang Xiao-Jun, Ju Hai-Yue. Hemodynamics model of fluid–solid interaction in internal carotid artery aneurysms[J]. Neurosurg Rev, 2011, 34(1): 39–47.
  [6] Fernando Mut, Rainald L?hner, Aichi Chien, Satoshi Tateshima, Fernando Vi?uela, Christopher Putman, Juan Cebral. Computational Hemodynamics Framework for the Analysis of Cerebral Aneurysms[J]. Int j numer method biomed eng, 2011, 27(6): 822-839.
  [7] Daniel M. Sforza, Christopher M. Putman, Juan R. Cebral. Computational fluid dynamics in brain aneurysms[J]. Int J Numer Method Biomed Eng, 2012, 28(0): 801-808.   [8] Fatma Gulden Simsek, Young W. Kwon. Investigation of material modeling in fluid–structure interaction analysis of an idealized three-layered abdominal aorta: aneurysm initiation and fully developed aneurysms[J]. J Biol Phys, 2015, 41(2): 173-201.
  [9] Paris Perdikaris, Joseph A. Insley, Leopold Grinberg, Yue Yu, Michael E. Papka, George Em. Karniadakis. Visualizing multiphysics, fluid-structure interaction phenomena in intracranial aneurysms[J]. Parallel Comput, 2016, 55: 9-16.
  [10] Tianlun Qiu, Guoliang Jin, Wuqiao Bao, Haitao Lu. Intercorrelations of morphology with hemodynamics in intracranial aneurysms in computational fluid dynamics[J]. Neurosciences, 2017, 22(3): 205-212.
  [11] Kristian Valen-Sendstad, Aslak W. Bergersen, Yuji Shimogonya, et.al. Real-World Variability in the Prediction of Intracranial Aneurysm Wall Shear Stress: The 2015 International Aneurysm CFD Challenge[J]. Cardiovasc Eng Technol, 2018; 9(4): 544-564.
  [12] Yunling Long, Jingru Zhong, Hongyu Yu, Huagang Yan, Zhizheng Zhuo, Qianqian Meng, Xinjian Yang, Haiyun Li. A scaling aneurysm model-based approach to assessing the role of flow pattern and energy loss in aneurysm rupture prediction[J]. J Transl Med, 2015; 13: 311.
  [13] Gambaruto AM, Janela J, Moura A, et al. Sensitivity of hemodynamics in a patient specific cerebral aneurysm to vascular geometry and blood rheology[J]. Math Biosci Eng, 2011, 8(2): 409-423.
  [14] Cebral JR, Meng H. Counterpoint: realizing the clinical utility of computational fluid dynamics-closing the gap[J]. AJNR Am J Neuroradiol, 2012, 33(3): 396-398.
  [15] Xiang J, Natarajan SK, Tremmel M, Ma D, Mocco J, Hopkins LN, Siddiqui AH, Levy EI, Meng H. Hemodynamic morphologic discriminants for intracranial aneurysm rupture[J]. Stroke J Cereb Circ, 2011, 42(1): 144-152.
  [16] Lu G, Huang L, Zhang XL, Wang SZ, Hong Y, Hu Z, Geng DY. Influence of hemodynamic factors on rupture of intracranial aneurysms: patient-specific 3D mirror aneurysms model computational fluid dynamics simulation[J]. AJNR Am J Neuroradiol, 2011, 32(7): 1255-1261.
  [17] Jia Lu, Shouhua Hu, Madhavan L. Raghavan. A shell- based inverse approach of stress analysis in intracranial aneurysms[J]. Ann Biomed Eng, 2013, 41(7): 1505-1515.
  [18] Xu Bai-Nan, Wang Fu-Yu, Liu Lei, Zhang Xiao-Jun, Ju Hai-Yue. Hemodynamics model of fluid–solid interaction in internal carotid artery aneurysms[J]. Neurosurg Rev, 2011, 34(1): 39-47.   [19] Fatma Gulden Simsek, Young W. Kwon. Investigation of material modeling in fluid structure interaction analysis of an idealized three-layered abdominal aorta: aneurysm initiation and fully developed aneurysms[J]. J Biol Phys, 2015, 41(2): 173-201.
  [20] Yue Yu, Paris Perdikaris, George Em Karniadakis. Fractional modeling of viscoelasticity in 3D cerebral arteries and aneurysms[J]. J Comput Phys, 2016, 323: 219-242.
  [21] J.D. Humphrey, G.A. Holzapfel. Mechanics, Mechanobiology, and Modeling of Human Abdominal Aorta and Aneurysms[J]. J Biomech, 2012, 45(5): 805-814.
  [22] Malebogo N. Ngoepe, Alejandro F. Frangi, James V. Byrne, Yiannis Ventikos. Thrombosis in Cerebral Aneurysms and the Computational Modeling Thereof: A Review Front Physiol[J]. 2018, 9: 306.
  [23] Michael J Bonares, A Leonardo de Oliveira Manoel, R Loch Macdonald, Tom A Schweizer.Behavioral profile of unruptured intracranial aneurysms: a systematic review[J]. Ann Clin Transl Neurol, 2014, 1(3): 220-232.
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