目的:探讨双容积图像融合技术在颅内动脉瘤介入血管内栓塞术中的应用价值。方法:对梅州市人民医院介入手术室2016年1-6月46例颅内动脉瘤破裂出血患者,栓塞前均行2D-DSA及3D-DSA检查,确定责任动脉瘤后行颅内动脉瘤介入栓塞术,术者认为栓...目的:探讨双容积图像融合技术在颅内动脉瘤介入血管内栓塞术中的应用价值。方法:对梅州市人民医院介入手术室2016年1-6月46例颅内动脉瘤破裂出血患者,栓塞前均行2D-DSA及3D-DSA检查,确定责任动脉瘤后行颅内动脉瘤介入栓塞术,术者认为栓塞满意后即刻行3D-DSA复查,应用数字减影三维重建技术中的双容积图像融合技术在AW(Advantage Workstation;GE Medical System)后处理工作站进行图像融合,评价动脉瘤栓塞的效果。结果:46例患者共发现52枚动脉瘤(4例为多发动脉瘤),均顺利进行介入栓塞术(其中38枚动脉瘤单纯行弹簧圈栓塞,14枚动脉瘤在支架或球囊辅助下栓塞),均在AW后处理工作站上完成图像融合,在融合图像的46例患者中:30例动脉瘤内无造影剂充填,10例瘤颈处见造影剂显影,瘤体部无造影剂显影,6例瘤体内见造影剂显影。结论:根据双容积图像融合技术来评估动脉瘤栓塞术后的残留情况来判断弹簧圈栓塞的致密程度及残留瘤腔大小,有助于术者判断动脉瘤栓塞术后是否继续进行栓塞或指导下一步的治疗方案,并且为以后的术后随访复查提供重要信息。展开更多
In the long distance transportation of slurry filled for mining filling,there exist complex variation rules of pressure and flow velocity,pipe distribution location and other influencing factors.Electrical capacitance...In the long distance transportation of slurry filled for mining filling,there exist complex variation rules of pressure and flow velocity,pipe distribution location and other influencing factors.Electrical capacitance tomography(ECT)is a technique for visualizing two-phase flow in a pipe or closed container.In this paper,a visual detection method was proposed by image reconstruction of core,laminar,bubble and annular flow based on ECT technology,which reflects distribution of slurry in deep filling pipeline and measures the degree of blockage.There is an error between the measured and the real two-phase flow distribution due to two factors,which are immature image reconstruction algorithm of ECT and difference of flow patterns leading to degrees of error.In this paper,convolutional neural networks(CNN)is used to recognize flow patterns,and then the optimal image is calculated by the improved particle swarm optimization(PSO)algorithm with weights using simulated annealing strategy,and the fitness function is improved based on the results of the shallow neural network.Finally,the reconstructed binary image is further processed to obtain the position,size and direction of the blocked pipe.The realization of this method provides technical support for pipeline detection technology.展开更多
文摘目的:探讨双容积图像融合技术在颅内动脉瘤介入血管内栓塞术中的应用价值。方法:对梅州市人民医院介入手术室2016年1-6月46例颅内动脉瘤破裂出血患者,栓塞前均行2D-DSA及3D-DSA检查,确定责任动脉瘤后行颅内动脉瘤介入栓塞术,术者认为栓塞满意后即刻行3D-DSA复查,应用数字减影三维重建技术中的双容积图像融合技术在AW(Advantage Workstation;GE Medical System)后处理工作站进行图像融合,评价动脉瘤栓塞的效果。结果:46例患者共发现52枚动脉瘤(4例为多发动脉瘤),均顺利进行介入栓塞术(其中38枚动脉瘤单纯行弹簧圈栓塞,14枚动脉瘤在支架或球囊辅助下栓塞),均在AW后处理工作站上完成图像融合,在融合图像的46例患者中:30例动脉瘤内无造影剂充填,10例瘤颈处见造影剂显影,瘤体部无造影剂显影,6例瘤体内见造影剂显影。结论:根据双容积图像融合技术来评估动脉瘤栓塞术后的残留情况来判断弹簧圈栓塞的致密程度及残留瘤腔大小,有助于术者判断动脉瘤栓塞术后是否继续进行栓塞或指导下一步的治疗方案,并且为以后的术后随访复查提供重要信息。
基金Project(51704229)supported by the National Natural Science Foundation of ChinaProject(2018YQ2-01)supported by the Outstanding Youth Science Fund of Xi’an University of Science and Technology,China。
文摘In the long distance transportation of slurry filled for mining filling,there exist complex variation rules of pressure and flow velocity,pipe distribution location and other influencing factors.Electrical capacitance tomography(ECT)is a technique for visualizing two-phase flow in a pipe or closed container.In this paper,a visual detection method was proposed by image reconstruction of core,laminar,bubble and annular flow based on ECT technology,which reflects distribution of slurry in deep filling pipeline and measures the degree of blockage.There is an error between the measured and the real two-phase flow distribution due to two factors,which are immature image reconstruction algorithm of ECT and difference of flow patterns leading to degrees of error.In this paper,convolutional neural networks(CNN)is used to recognize flow patterns,and then the optimal image is calculated by the improved particle swarm optimization(PSO)algorithm with weights using simulated annealing strategy,and the fitness function is improved based on the results of the shallow neural network.Finally,the reconstructed binary image is further processed to obtain the position,size and direction of the blocked pipe.The realization of this method provides technical support for pipeline detection technology.
