一种硅油填充术眼内硅油乳化过程模拟可视化方法

班晓娟; 王佳敏; 王笑琨; 张雅斓; 徐衍睿; 宋重明; 黄厚斌; 朱志鸿

doi:10.11999/JEIT210919

一种硅油填充术眼内硅油乳化过程模拟可视化方法

doi: 10.11999/JEIT210919

班晓娟^{1, 2, 3},
王佳敏²,
王笑琨^{1, 2, 3, ,},
张雅斓^{1, 2, 3},
徐衍睿^{1, 2, 3},
宋重明²,
黄厚斌⁴,
朱志鸿⁴

1.
北京材料基因工程高精尖创新中心(北京科技大学) 北京 100083
2.
计算机与通信工程学院、人工智能研究院(北京科技大学) 北京 100083
3.
材料领域知识工程北京市重点实验室(北京科技大学) 北京 100083
4.
中国人民解放军总医院海南医院三亚 572013

基金项目: 国家自然科学基金(61873299)，海南省财政科技计划(ZDYF2020031, ZDYF2019009)，广东省自然科学基金(2021A1515012285)，佛山市人民政府科技创新专项资金(BK20AF001, BK19AE034)，中央高校基本科研基金(FRF-TP-19-043-A2)

详细信息

作者简介:
班晓娟：女，1970年生，教授，研究方向为人工智能、计算机图形学

王佳敏：女，2000年生，硕士生，研究方向为计算机图形学流体模拟

王笑琨：男，1987年生，副教授，研究方向为计算机图形学

张雅斓：女，1992年生，博士后，研究方向为计算机图形学

徐衍睿：男，1995年生，博士生，研究方向为计算机图形学

宋重明：女，1998年生，硕士生，研究方向为计算机图形学流体模拟

黄厚斌：男，1973年生，主任医师，研究方向为眼科

朱志鸿：男，1990年生，主治医师，研究方向为眼科

通讯作者:
王笑琨　wangxiaokun@ustb.edu.cn

中图分类号: R774.1;TP399
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- 被引次数: 0
出版历程
- 收稿日期: 2021-09-01
- 修回日期: 2021-12-27
- 录用日期: 2021-12-28
- 网络出版日期: 2022-01-05
- 刊出日期: 2022-01-10

A Computer Simulation and Visualization Method of Intraocular Silicone Oil Emulsification Process of Silicone Oil Tamponade

BAN Xiaojuan^{1, 2, 3},
WANG Jiamin²,
WANG Xiaokun^{1, 2, 3
, ,},
ZHANG Yalan^{1, 2, 3},
XU Yanrui^{1, 2, 3},
SONG Chongming²,
HUANG Houbin⁴,
ZHU Zhihong⁴

1.
Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
School of Computer and Communication Engineering, Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China
3.
Beijing Key Laboratory of Knowledge Engineering for Materials Science, University of Science and Technology Beijing, Beijing 100083, China
4.
Hainan Hospital of PLA General Hospital, Sanya 572013, China

Funds: The National Natural Science Foundation of China (61873299), The Key Research Plan of Hainan Province (ZDYF2020031, ZDYF2019009), The Natural Science Foundation of Guangdong(2021A1515012285), The Scientific and Technological Innovation Foundation of Shunde Graduate School, USTB (BK20AF001, BK19AE034), The Fundamental Research Funds for the Central Universities of China (FRF-TP-19-043-A2)

摘要

摘要: 眼科中治疗孔源性视网膜脱离(RRD)的玻璃体切割联合硅油填充术中，预测术后硅油乳化情况，并根据其确定术中适宜硅油填充量和最终取出时间是手术成功的关键。然而手术流程中医生无法直接观察眼球内部结构，很难对填充物质进行定量分析，手术完成后亦对硅油乳化状态缺乏可视化认知。该文提出一种基于光滑粒子流体动力学的体积不可压缩多相流体计算框架，结合表面张力模型对眼内环境中硅油和水的耦合进行数值计算；构建以力平衡散体动力学模型为基础的多相流体互溶扩散模拟方法，对硅油乳化过程进行可视量化分析。实验表明，该方法可以稳定模拟强表面张力作用下多相流体耦合以及可混溶多相流体相间交互效果，从而有效辅助医生判定所需硅油填充量，并预测评估硅油乳化状态对手术预后的影响。
- 医学可视化 /
- 计算机辅助诊断 /
- 孔源性视网膜脱离 /
- 多相流模拟
Abstract: In vitrectomy combined with silicone oil tamponade for the treatment of Rhegmatogenous Retinal Detachment(RRD) in ophthalmology, the prediction of postoperative silicone oil emulsification, the appropriate amount of silicone oil filling and the final removal time are the key to the success of the operation. However, doctors can not directly observe the internal structure of the eyeball during the operation process, and it is difficult to analyze quantitatively the filling materials. After the operation, there is also a lack of visual cognition of the emulsification state of silicone oil. In this paper, a volume incompressible multiphase fluid computational framework based on Smooth Particle Hydrodynamics is proposed to calculate numerically the coupling of silicone oil and water in intraocular environment, combined with the surface tension model. A multiphase fluid miscible diffusion simulation method based on the force-balanced dispersion dynamics model is constructed to analyze visually and quantitatively the silicone oil emulsification process. Experiments show that this method can simulate the multiphase fluid coupling under strong surface tension and interphase interaction of miscible multiphase fluid stably, so as to assist effectively the doctors to determine the amount of silicone oil required before operation, and predict and evaluate the influence of silicone oil emulsification state on the prognosis of operation.
- Medical visualization /
- Computer aided diagnosis /
- Rhegmatogenous Retinal Detachment(RRD) /
- Multiphase flows simulation

HTML全文

图 1 SPH数值近似示意图

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图 2 可混溶硅油-水耦合乳化模拟框架

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图 3 具有混合相的粒子

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图 4 瑞利泰勒不稳定性实验

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图 5 DFSPH和本文方法迭代次数对比

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图 6 墨滴实验

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图 7 硅油乳化实验

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参考文献(26)

[1]	金琴辉, 张昕, 杨友谊. 硅油眼内填充后对视网膜及视神经的影响[J]. 中国眼耳鼻喉科杂志, 2019, 19(3): 212–216. doi: 10.14166/j.issn.1671-2420.2019.03.022 JIN Qinhui, ZHANG Xin, and YANG Youyi. Effects of intraocular tamponade with silicone oil on retina and optic nerve[J]. Chinese Journal of Ophthalmology and Otorhinolaryngology, 2019, 19(3): 212–216. doi: 10.14166/j.issn.1671-2420.2019.03.022
[2]	何勤, 柯根杰. 硅油填充术后早期硅油进入前房的临床分析及处理[J]. 临床眼科杂志, 2020, 28(4): 316–318. doi: 10.3969/j.issn.1006-8422.2020.04.007 HE Qin and KE Genjie. Analysis and treatment of the clinical characteristics of early silicone oil entering the anterior chamber after silicone oil filling[J]. Journal of Clinical Ophthalmology, 2020, 28(4): 316–318. doi: 10.3969/j.issn.1006-8422.2020.04.007
[3]	蔡轶珩, 高旭蓉, 邱长炎, 等. 一种混合特征高效融合的视网膜血管分割方法[J]. 电子与信息学报, 2017, 39(8): 1956–1963. doi: 10.11999/JEIT161290 CAI Yiheng, GAO Xurong, QIU Changyan, et al. Retinal vessel segmentation method with efficient hybrid features fusion[J]. Journal of Electronics &Information Technology, 2017, 39(8): 1956–1963. doi: 10.11999/JEIT161290
[4]	陈强, 徐军, 牛四杰. 基于随机森林的频谱域光学相干层析技术的图像视网膜神经纤维层分割[J]. 电子与信息学报, 2017, 39(5): 1101–1108. doi: 10.11999/JEIT160663 CHEN Qiang, XU Jun, and NIU Sijie. Retinal nerve fiber layer segmentation of spectral domain optical coherence tomography images based on random forest[J]. Journal of Electronics &Information Technology, 2017, 39(5): 1101–1108. doi: 10.11999/JEIT160663
[5]	徐衍睿, 班晓娟, 王笑琨, 等. 面向视网膜脱离手术的硅油填充模拟[J]. 工程科学学报, 2021, 43(9): 1233–1243. doi: 10.13374/j.issn2095-9389.2021.01.13.006 XU Yanrui, BAN Xiaojuan, WANG Xiaokun, et al. Simulations of silicone oil filling for use in retinal detachment surgery[J]. Chinese Journal of Engineering, 2021, 43(9): 1233–1243. doi: 10.13374/j.issn2095-9389.2021.01.13.006
[6]	BARGTEIL A W, SHINAR T, and KRY P G. An introduction to physics-based animation[C]. SIGGRAPH Asia 2020 Courses, 2020: 1–57.
[7]	RAO Chengping, SUN Hao, and LIU Yang. Physics-informed deep learning for incompressible laminar flows[J]. Theoretical and Applied Mechanics Letters, 2020, 10(3): 207–212. doi: 10.1016/j.taml.2020.01.039
[8]	张雅斓, 班晓娟, 徐衍睿, 等. 面向非牛顿流体仿真的边界处理方法[J]. 计算机辅助设计与图形学学报, 2019, 31(8): 1341–1349. doi: 10.3724/SP.J.1089.2019.17576 ZHANG Yalan, BAN Xiaojuan, XU Yanrui, et al. Boundary handling for Non-Newtonian fluid simulation[J]. Journal of Computer-Aided Design &Computer Graphics, 2019, 31(8): 1341–1349. doi: 10.3724/SP.J.1089.2019.17576
[9]	SKRIVAN T, SODERSTROM A, JOHANSSON J, et al. Wave curves: Simulating lagrangian water waves on dynamically deforming surfaces[J]. ACM Transactions on Graphics, 2020, 39(4): 65. doi: 10.1145/3386569.3392466
[10]	MONAGHAN J J. Smoothed particle hydrodynamics[J]. Reports on Progress in Physics, 2005, 68(8): 1703–1759. doi: 10.1088/0034-4885/68/8/R01
[11]	BECKER M and TESCHNER M. Weakly compressible SPH for free surface flows[C]. 2007 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, San Diego, USA, 2007: 209–217.
[12]	SOLENTHALER B and PAJAROLA R. Predictive-corrective incompressible SPH[J]. ACM Transactions on Graphics, 2009, 28(3): 40. doi: 10.1145/1531326.1531346
[13]	IHMSEN M, CORNELIS J, SOLENTHALER B, et al. Implicit Incompressible SPH[J]. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(3): 426–435. doi: 10.1109/TVCG.2013.105
[14]	BENDER J and KOSCHIER D. Divergence-free smoothed particle hydrodynamics[C]. The 14th ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Los Angeles, USA, 2015: 147–155.
[15]	WANG Xiaokun, LIU Sinuo, BAN Xiaojuan, et al. Robust turbulence simulation for particle-based fluids using the Rankine vortex model[J]. The Visual Computer, 2020, 36(10): 2285–2298. doi: 10.1007/s00371-020-01914-5
[16]	LIU Sinuo, WANG Xiaokun, BAN Xiaojuan, et al. Turbulent details simulation for SPH Fluids via Vorticity refinement[J]. Computer Graphics Forum, 2021, 40(1): 54–67. doi: 10.1111/cgf.14095
[17]	MÜLLER M, SOLENTHALER B, KEISER R, et al. Particle-based fluid-fluid interaction[C]. 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation, Los Angeles, USA, 2005: 237–244.
[18]	LIU Shiguang, LIU Qiguang, and PENG Qunsheng. Realistic simulation of mixing fluids[J]. The Visual Computer, 2011, 27(3): 241–248. doi: 10.1007/s00371-010-0531-1
[19]	REN Bo, LI Chenfeng, YAN Xiao, et al. Multiple-fluid SPH Simulation using a mixture model[J]. ACM Transactions on Graphics, 2014, 33(5): 171. doi: 10.1145/2645703
[20]	YANG Tao, CHANG Jian, REN Bo, et al. Fast multiple-fluid simulation using Helmholtz free energy[J]. ACM Transactions on Graphics, 2015, 34(6): 201. doi: 10.1145/2816795.2818117
[21]	YAN Xiao, JIANG Yuntao, LI Chenfeng, et al. Multiphase SPH simulation for interactive fluids and solids[J]. ACM Transactions on Graphics, 2016, 35(4): 79. doi: 10.1145/2897824.2925897
[22]	JIANG Y, LI C, DENG S, et al. A divergence-free mixture model for multiphase fluids[J]. Computer Graphics Forum, 2020, 39(8): 69–77. doi: 10.1111/cgf.14102
[23]	BAND S, GISSLER C, IHMSEN M, et al. Pressure boundaries for implicit incompressible SPH[J]. ACM Transactions on Graphics, 2018, 37(2): 14. doi: 10.1145/3180486
[24]	AKINCI N, AKINCI G, and TESCHNER M. Versatile surface tension and adhesion for SPH fluids[J]. ACM Transactions on Graphics, 2013, 32(6): 182. doi: 10.1145/2508363.2508395
[25]	MANNINEN M, TAIVASSALO V, and KALLIO S. On the mixture model for multiphase flow[R]. VTT Publications 288, 1996.
[26]	王笑琨, 班晓娟, 刘旭, 等. 面向SPH流体的高效各向异性表面重构算法[J]. 计算机辅助设计与图形学学报, 2016, 28(9): 1497–1505. WANG Xiaokun, BAN Xiaojuan, LIU Xu, et al. Effective reconstructing surfaces algorithm of anisotropic kernels orienting SPH fluids[J] Journal of Computer-Aided Design & Computer Graphics, 2016, 28(9): 1497–1505.