Image Fusion of Dyna CT and Digital Subtraction Angiography for Arteriovenous Malformations_Journal of Biomedical Engineering

Authors：

LIANYuxi ¹ , YUJinhua ¹ ,  WANGYuanyuan ^1,2 , CHENLiang ³ , SONGJianping ³ , SHIZhifeng ³

1. Department of Electronic Engineering, Fudan University, Shanghai 200433, China;
2. Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai 200032, China;
3. Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200400, China;

Corresponding author：

WANGYuanyuan, Email: yywang@fudan.edu.cn

Keywords：

image fusion; arteriovenous malformations; Dyna CT; digital subtraction angiography

DOI：

10.7507/1001-5515.20160141

Video：

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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 three-dimensional 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.

Citation： LIAN Yuxi, YU Jinhua, WANG Yuanyuan, CHEN Liang, SONG Jianping, SHI Zhifeng. Image Fusion of Dyna CT and Digital Subtraction Angiography for Arteriovenous Malformations. Journal of Biomedical Engineering, 2016, 33(5): 873-878. doi: 10.7507/1001-5515.20160141 Copy

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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.

1. GABRIEL R A, KIM H, SIDNEY S, et al. Ten-year detection rate of brain arteriovenous malformations in a large, multiethnic, defined population[J]. Stroke, 2010, 41(1):21-26.
2. BOSTRÖEM 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-3D image 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.

Journal of Biomedical Engineering

Image Fusion of Dyna CT and Digital Subtraction Angiography for Arteriovenous Malformations

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