脑动静脉畸形Dyna CT和数字减影血管造影的图像融合
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摘要
Dyna CT和数字减影血管造影(DSA)成像模态对脑动静脉畸形的诊断和治疗具有重要价值。三维Dyna CT可以提供动静脉畸形团的空间信息,而二维DSA可以提供判别动静脉的时间信息。为了同时显示动静脉及畸形团的位置关系,需要对这两种模态的图像进行融合。本文提出一种二维到三维反投影生长的方法,实现了二维DSA图像和三维Dyna CT图像的融合,从而可以在三维Dyna CT图像中直观地区分动静脉。实验结果表明,融合图像既能提供畸形团的空间位置信息,又能提供动静脉流向的时间信息,可以帮助医生更准确地诊断脑动静脉畸形以及进行手术规划。
Both Dyna CT,a rotational faultage reconstructed technique,and digital subtraction angiography(DSA)play important roles in the diagnosis and treatment of arteriovenous malformations(AVM).Three-dimensional Dyna CT can provide the spatial information of AVM nidus,while two-dimensional DSA can provide the time information for distinguishing arteries and veins.To illustrate the location relationship of the nidus,arteries and veins at the same time,these two imaging modalities need to be fused.In this paper,a two-dimensional to three-dimensional back projection and growing method is proposed,which realizes the image fusion of two-dimensional DSA and threedimensional Dyna CT and achieves the differentiation of arteries and veins in Dyna CT.The experimental results showed that the fusion image could present both the position information of AVM nidus and the dynamic information of the blood vessels.Therefore,the proposed method can help surgeons locate the AVM abnormality and make operation plan more accurately.
Both Dyna CT,a rotational faultage reconstructed technique,and digital subtraction angiography(DSA)play important roles in the diagnosis and treatment of arteriovenous malformations(AVM).Three-dimensional Dyna CT can provide the spatial information of AVM nidus,while two-dimensional DSA can provide the time information for distinguishing arteries and veins.To illustrate the location relationship of the nidus,arteries and veins at the same time,these two imaging modalities need to be fused.In this paper,a two-dimensional to three-dimensional back projection and growing method is proposed,which realizes the image fusion of two-dimensional DSA and threedimensional Dyna CT and achieves the differentiation of arteries and veins in Dyna CT.The experimental results showed that the fusion image could present both the position information of AVM nidus and the dynamic information of the blood vessels.Therefore,the proposed method can help surgeons locate the AVM abnormality and make operation plan more accurately.
引文
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[2]BOSTREM J,HADIZADEH D R,BLOCK W A,et al.Magnetic resonance spectroscopic study of radiogenic changes after radiosurgery of cerebral arteriovenous malformations with implications for the differential diagnosis of radionecrosis[J].Radiation Oncology,2013,8:54-63.
[3]BOVA F,MEEKS S,FRIDMAN W.Linac radiosurgery:system requirements,procedures,and testing[M]//KHAN F.Treatment planning in radiation oncology.Second ed.Philadelphia:Lippincott Williams&Wilkins,2007:189-211.
[4]赵书河,李培军,冯学智.遥感影像决策既融合方法实验研究[J].测绘科学技术学报,2008,24(4):247-256.
[5]武杰,聂生东,汪红志,等.基于局部特征的医学图像融合方法[J].中国医学影像技术,2009,25(3):496-498.
[6]夏明革,何友,欧阳文.像素级图像融合方法与融合效果评价[J].遥感技术与应用,2002,17(4):224-229.
[7]DAS S,KUNDU M K.NSCT-based multimodal medical image fusion using pulse-coupled neural network and modified spatial frequency[J].Med Biol Eng Comput,2012,50(10):1105-1114.
[8]WANG Zhaobin,MA Yide,GU J.Multi-focus image fusion using PCNN[J].Pattern Recognit,2010,43(6):2003-2016.
[9]HIPWELL J H,PENNEY G P,MCLAUGHLIN R A,et al.Intensity-based 2-D-3-D registration of cerebral angiograms[J].IEEE Trans Med Imaging,2003,22(11):1417-1426.
[10]LU X Q.2D-3Dimage registration[C]//Proceedings of International Conference on Biomedical Engineering and Informatics.Shanghai,2011:1-4.
[11]OTSU N.A threshold selection method from gray-level histograms[J].IEEE Trans Syst Man Cybern,1979,9(1):62-66.
[12]AZAR F S,YODH A G,CHOE R,et al.Coregistration and analysis of multi-modal images obtained in different geometries:US,US20090135191[P].2009-05-28.
[13]GUO Y,LU C C.Multi-modality image registration using mutual information based on gradient vector flow[C]//Proceedings ofInternational Conference of IEEE Computer Societyon Pattern Recognition.Hong Kong,2006:697-700.
[14]VON KLINSKI S,DERZ C,WEESE D,et al.Model based image processing using snakes and mutual information[J].Proc SPIE of Medical Imaging:Image Processing,2000:1053-1064.
[15]李彬,欧陕兴,田联房,等.基于自适应自由变形法和梯度下降法的胸部多模医学图像配准[J].计算机应用研究,2009,26(10):3978-3982.
[16]MAES F,COLLIGNON A,VANDERMEULEN D,et al.Multimodality image registration by maximization of mutual information[J].IEEE Trans Med Imaging,1997,16(2):187-198.