摘要
提高胃结肠癌症早期诊断的准确性和效率具有重要意义。目前以虚拟内窥镜为代表的一系列非侵入性的检查方法已逐渐成为胃肠道息肉检查和癌症早期诊断的常用技术。其中,近年来新提出的虚拟外翻技术能够通过外视的视角观察原内壁表面信息。观察经虚拟外翻后的结肠数据,视野明显扩大,视野面积数倍于虚拟内窥镜,而对病灶的定位也更为直观。这些特点克服了原有虚拟内窥镜和虚拟展平技术的局限性,使得虚拟外翻技术具有重要的实用价值。其应用前景经专家论证,已获得了国家863项目基金资助。
然而虚拟外翻技术作为一项全新的虚拟可视化技术,还存在一些问题亟待完善以便更好地应用到临床实践。为了改进虚拟外翻算法的处理结果,本文提出了以完整体数据作为外翻对象的虚拟外翻算法。经模型数据和人体CT扫描数据上测试,体数据外翻结果明显改善了原有二值化数据外翻结果中数据表面粗糙不光滑或者结构破裂的现象,进而可以提高胃结肠息肉检查的准确率和效率,降低检查者的视觉疲劳。围绕体数据外翻算法,改进和优化了与体数据外翻相关的外翻基准面、中心路径、体数据采样网格等外翻关键材料的处理算法,并验证了各算法的有效性,分析了它们在胃和肠道中的适用特性。
基于测地距离变换设计的局部膨胀腐蚀算法能够很好地避免原有膨胀腐蚀算法造成的基准面粘连问题;而利用了混合电场模型的虚拟外翻方法较原有基于局部电场模型的外翻方法大大减少了采样网格的计算消耗。通过分析各数据采样网格模型和外翻算法,本文总结出:通过中心路径外翻算法和混合电场模型采样网格更适合结肠内壁的虚拟外翻,而基准面外翻算法和符号距离函数采样网格更适合胃部内壁的虚拟外翻。
关于外翻数据的检查方法,本文基于数据采样网格设计了两种专用于虚拟外翻数据检查的导航路径:平行导航线以及螺旋导航线。设计并研究了螺旋导航检查、定点检查、分段检查和多导航路径并行检查等多种能够遍历整个结肠外翻数据表面的导航检查模式,分析了检查模式中各参数之间的定量关系,推算出螺旋导航检查和定点检查中推荐的参数设定,为虚拟外翻数据应用于临床实践打好基础。
关于外翻数据检查效果评估,利用体数据外翻方法对模型数据和真实CT数据实施外翻后,将虚拟外翻数据的息肉检查和虚拟内窥镜息肉检查进行了对照实验比较。结果表明无论在模型数据还是真实CT数据上,虚拟外翻数据检查都比虚拟内窥镜方法大幅降低了检查时间(在模型中平均减少了65.0%,在CT数据中平均减少了72.1%)。同时基本保持了对息肉检查的敏感性、特异性水平。虚拟外翻技术可以作为一种补充的非侵入式技术应用于胃肠道息肉检查。甚至和虚拟内窥镜、虚拟展平技术等融合在一起,组成功能更完整的胃肠道癌症诊断系统。
To develop the observation techniques of stomach and colon will result in an increase in the diagnostic availability of the stomach and colon cancer. Many visualization methods for non-invasive gastrointestinal (GI) tract diagnosis been proposed. Some of which have been developed and adopted in clinical applications, such as virtual colonoscopy and virtual flattening. The virtual eversion method allows the observation of the colonic mucosa under a global view while keeping the colon path preserved. Reviewing the everted data, the view angle is fairly expanded, the locations of the lesions are more intuitive. All of these make the virtual eversion overcome the limitations in the virtual colonoscopy and virtual flattening.
However, as a developing visualization methods, the following issues still need to be addressed before virtual eversion can be used in clinical applications. The virtual eversion method for GI volume data was proposed to perfect the eversion result. The volume data based eversion method resulted a much smoother surface, compared with the old binary data based eversion method. The smooth result may improve the accuracy of polyps detection. A set of key material for volume data eversion were presented in this paper, e.g. eversion reference surface, centerline, volume data sampling grid. The relevant algorithms were validated and analyzed.
The proposed local dilation and erosion algorithm, which based on the geodesic distance transformation, resulted a perfect eversion reference surface without error connection in it. The data sampling grid based on hybrid electrical field model saved much processing time that cost by the old data sampling grid. The analytics on various data sampling grids and eversion methods resulted that, the eversion method based on centerline model and hybrid electrical field model was suitable for colon data eversion, and the method based on reference surface and signed distance function model was good for stomach eversion.
We designed two kinds of navigation path based on the data sampling grid, the parallel navigation path and the spiral navigation path, for observing the everted data. Several observation schemas (spiral navigation, check-points observation, section observation, and multiple path navigation) were researched. We analyzed the dependency of the parameters in the observation schemas, and figured out the suggested parameters. The schemas and parameters are important foundation for the everted data observation.
The volume eversion method were validated using 3D phantoms and CT data sets. A study on the observation performance of the everted data showed that, the reading times were (65.0% of time reduction for Phantoms, and 72.1% of time reduction for CT data) less than those using virtual colonoscopy, while maintaining the sensibility and specificity. The virtual eversion can be a complimentary method for non-invasive polyp inspection. Together with virtual colonoscopy and virtual flattening, the virtual eversion may be integrated to produce a powerful system for GI tract diagnosis.
引文
[1] Johnson, C., Dachman, A., CT colonography: the next colon screening examination?[J]. Radiology, 2000. 216(2): pp. 331-341.
[2] Johnson, C., Hara, A., Reed, J., Virtual endoscopy: What's in a name?[J]. American Journal of Roentgenology, 1998. 171(5): pp. 1201-1202.
[3]张振亚,赵泽贞,大肠癌流行病学研究现状及展望[J].肿瘤防治研究, 2000. 27(002): pp. 154-156.
[4]吕平,刘芳,吕坤章等,内窥镜发展史[J].中华医史杂志, 2002. 32(1): pp. 10-14.
[5]黄志强,胆石病——一个外科学家的实录2006,北京:清华大学出版社.
[6] Wang, G., McFarland, E.G., Brown, B.P., et al., GI tract unraveling with curved cross sections[J]. IEEE Transactions on Medical Imaging, 1998. 17(2): pp. 318-322.
[7] Bartrol, A., Wegenkittl, R., K nig, A., et al., Nonlinear virtual colon unfolding, in Proceedings of the conference on Visualization'01. 2001, IEEE Computer Society Washington, DC, USA: DC, USA. pp. 411-420.
[8] Fletcher, J.G., Johnson, C.D., Reed, J.E., et al., Feasibility of planar virtual pathology: A new paradigm in volume-rendered CT colonography[J]. Journal of Computer Assisted Tomography, 2001. 25(6): pp. 864-869.
[9] Vining, D.J., Virtual endoscopy: Is it reality?[J]. Radiology, 1996. 200(1): pp. 30-31.
[10] Vining, D.J., Hemler, P.F., Stelts, D.R., et al., Virtual endoscopy: Quicker and easier disease evaluation[J]. Physiology and Function from Multidimensional Images - Medical Imaging 1997, 1997. 3033: pp. 415-423.
[11] Vining, D.J., Liu, K., Choplin, R.H., et al., Virtual bronchoscopy: Relationships of virtual reality endobronchial simulations to actual bronchoscopic findings[J]. Chest, 1996. 109(2): pp. 549-553.
[12] Vining, D.J., Stelts, D.R., Ahn, D.K., et al., Freeflight: A virtual endoscopy system[J]. Cvrmed-Mrcas'97, 1997. 1205: pp. 413-416.
[13] Hassouna, M., Farag, A., Falk, R., Virtual Fly-Over: A New Visualization Technique for Virtual Colonoscopy, in Medical Image Computing and Computer-Assisted Intervention (MICCAI 2006). 2006, Springer: Copenhagen, Denmark. pp. 381-388.
[14] Zhao, J., Cao, L., Zhuang, T., et al., Digital eversion of a hollow structure: an application in virtual colonography[J]. Int J Biomed Imaging, 2008. 2008: pp. 763028.
[15]曹立基,赵俊,人体空腔结构可视化技术[J].国际生物医学工程杂志, 2008. 31(3): pp. 180-183.
[16]黄翊玲,赵俊,曹立基,基于基准面全局电场的外翻快速算法[J].系统仿真学报, 2009. 21(23): pp. 7515-7517.
[17]林洲,赵俊,曹立基,结肠的快速虚拟外翻方法[J].中国医疗器械杂志, 2008. 32(6): pp. 394-397.
[18]张丹枫,赵俊,李雷等,一种结肠的自动分段虚拟外翻技术研究[J].中国医疗器械杂志, 2010. 34(2): pp. 79-81.
[19] Hunt, G.W., Hemler, P.F., Vining, D.J., Automated virtual colonoscopy[J]. Image Display - Medical Imaging 1997, 1997. 3031: pp. 535-541.
[20] Beaulieu, C., Jeffrey Jr, R., Karadi, C., et al., Display modes for CT colonography: Part II. blinded comparison of axial CT and virtual endoscopic and panoramic endoscopic volume-rendered Studies[J]. Radiology, 1999. 212(1): pp. 203-212.
[21] Chen, S., Lu, D., Hecht, J., et al., CT colonography: Value of scanning in both the supine and prone positions[J]. American Journal of Roentgenology, 1999. 172(3): pp. 595-599.
[22] Young, B., Fletcher, J., Paulsen, S., et al., Polyp measurement with CT colonography: multiple-reader, multiple-workstation comparison[J]. American Journal of Roentgenology, 2007. 188(1): pp. 122-129.
[23] Lee, S., Park, S., Choi, E., et al., Colorectal polyps on portal phase contrast-enhanced CT colonography: lesion attenuation and distinction from tagged feces[J]. American Journal of Roentgenology, 2007. 189(1): pp. 35-40.
[24] Wang, G., Dave, S.B., Brown, B.P., et al., Colon unraveling based on electrical field - Recent progress and further work, in Medical Imaging 1999 Conference, C.T. Chen and A.V. Clough, Editors. 1999, Spie-Int Soc Optical Engineering: San Diego, Ca. pp. 125-132.
[25] Zhang, Z., Wang, G., Brown, B.P., et al., Fast algorithm for soft straightening of the colon[J]. Academic Radiology, 2000. 7(3): pp. 142-148.
[26] Zhang, Z., Wang, G., Brown, B.P., et al., Distortion reduction for fast soft straightening of the colon[J]. Academic Radiology, 2000. 7(7): pp. 506-515.
[27] Hong, W., Gu, X., Qiu, F., et al. Conformal virtual colon flattening. 2006: ACM.
[28] Haker, S., Angenent, S., Tannenbaum, A., et al., Nondistorting flattening maps and the 3-D visualization of colon CTimages[J]. IEEE Transactions on Medical Imaging, 2000. 19(7): pp. 665-670.
[29] Dave, S.B., Wang, G., Brown, B.P., et al., Straightening the colon with curved cross sections: An approach to CT colonography[J]. Acad Radiol, 1999. 6(7): pp. 398-410.
[30] Zhao, J., Cao, L.,Zhuang, T., 3D exoscopy via virtually everting the inner surface of a hollow organ. 2006: China Patent.
[31]章毓晋,图象工程/上册/图象处理和分析. 1999:清华大学出版社.
[32]龚声蓉,刘纯平,王强,数字图像处理与分析. 2006:清华大学出版社.
[33]姚敏,数字图像处理2006:机械工业出版社.
[34]章毓晋,图像工程,II.图像分析. 2005:清华大学出版社.
[35] Franaszek, M., Summers, R.M., Pickhardt, P.J., et al., Hybrid segmentation of colon filled with air and opacified fluid for CT colonography[J]. IEEE Transactions on Medical Imaging, 2006. 25(3): pp. 358-368.
[36] Lakare, S., Wan, M., Sato, M., et al. 3-D digital cleansing using segmentation rays. in Proc. Visualization. 2000.
[37] Nahed, J., Jolly, M.,Yang, G., Robust Active Shape Models: A Robust, Generic and Simple Automatic Segmentation Tool[J]. 2008.
[38] Van Uitert, R.L., Summers, R.M., Automatic correction of level set based subvoxel precise centerlines for virtual colonoscopy using the colon outer wall[J]. IEEE Transactions on Medical Imaging, 2007. 26(8): pp. 1069-1078.
[39] Herman, G.T., Fundamentals of Computerized Tomography: Image Reconstruction from Projections. 2nd ed. 2009: Springer.
[40]庄天戈, CT原理与算法. 1992:上海交通大学出版社.
[41]吴东,周康荣,彭卫军,不同对比剂用于螺旋CT胃部三维成像的对照研究[J].中华放射学杂志, 2001. 35(4): pp. 258-261.
[42]张镭,潘振宇, CT仿真胃内窥镜的临床应用研究[J].中华放射学杂志, 2000. 34(9): pp. 609-612.
[43] Pickhardt, P., Choi, J., Hwang, I., et al., Computed tomographic virtual colonoscopy to screen for colorectal neoplasia in asymptomatic adults[J]. New England Journal of Medicine, 2003. 349(23): pp. 2191.
[44]余深平,李子平,许达生等,大肠充气螺旋CT扫描图像后处理功能的临床应用[J].中华放射学杂志, 2000. 34(5): pp 295-299.
[45] Haralick, R.,Shapiro, L., Image segmentation techniques[J]. Computer vision, graphics, and image processing, 1985. 29(1): pp. 100-132.
[46] (美)Rafael C. Gonzalez, R.E.W.阮.,数字图像处理(第二版)(Digital Image Processing, Second Edition). 2003.3:电子工业出版社.
[47] Chang, Y., Li, X., Adaptive image region-growing[J]. Image Processing, IEEE Transactions on, 2002. 3(6): pp. 868-872.
[48] Osher, S., Sethian, J., Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations[J]. Journal of computational physics, 1988. 79(1): pp. 12-49.
[49] Sethian, J., Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science. 1999: Cambridge Univ Pr.
[50] Caselles, V., Kimmel, R.,Sapiro, G., Geodesic active contours[J]. International Journal of Computer Vision, 1997. 22(1): pp. 61-79.
[51] Leventon, M., Grimson, W.,Faugeras, O. Statistical shape influence in geodesic active contours. 2000: IEEE.
[52] Malladi, R., Sethian, J., Vemuri, B., Shape modeling with front propagation: A levelset approach[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1995. 17(2): pp. 158-175.
[53] Udupa, J., Samarasekera, S., Extraction of fuzzy object information in multidimensional images for quantifying ms lesions of the brain. 1998, United States Patent Office
[54] Lu, L., Zhang, D., Li, L., et al., Fully Automatic Segmentation of Colon for Virtual Colonoscpoy[J]. IEEE Transactions on Medical Imaging (under review), 2010.
[55] Kang, D.G.,Ra, J.B., A distance-field-based approach in generating cross-sections for 2-D vessel quantification, in Medical Imaging 2005 Conference, A.A.M.A. Amini, Editor. 2005, Spie-Int Soc Optical Engineering: San Diego, CA. pp. 747-758.
[56] Kang, D.G., Suh, D.C.,Ra, J.B., Three-dimensional blood vessel quantification via centerline deformation[J]. IEEE Transactions on Medical Imaging, 2009. 28(3): pp. 405-414.
[57] Zhang, D., Zhao, J., Lu, L., et al., Virtual eversion and rotation of colon based on outer surface centerline[J]. Medical Physics, 2010. 37(10): pp. 5518-5529.
[58] Wan M., Liang Z., Ke Q., et al., Automatic centerline extraction for virtual colonoscopy[J]. IEEE Transactions on Medical Imaging, 2002. 21(12): pp. 1450-60.
[59] Dong-Goo, K., Jaeyoun, Y.,Jong Beom, R., Automatic flight path generation in a virtual colonoscopy system[J]. Proceedings of the SPIE - The International Society for Optical Engineering, 2003. l5029: pp. 669-677.
[60] Zhang, D., Zhao, J., Lu, L., et al., A Virtual Eversion of Colon Based Outer-Surface Centerline, in Yangtze River 2009 International Congress on Medical Imaging Physics (ICMIP2009). 2009: NanJing, China. pp. 194-201.
[61] Borgefors, G., Distance transformations in digital images[J]. Computer vision, graphics, and image processing, 1986. 34(3): pp. 344-371.
[62] Danielsson, P., Euclidean distance mapping[J]. Computer Graphics and image processing, 1980. 14(3): pp. 227-248.
[63] Saito, T., Toriwaki, J., New algorithms for Euclidean distance transformation of an n-dimensional digitized picture with applications[J]. Pattern recognition, 1994. 27(11): pp. 1551-1565.
[64] Eggers, H., Two Fast Euclidean Distance Transformations in Z2Based on Sufficient Propagation[J]. Computer Vision and Image Understanding, 1998. 69(1): pp. 106-116.
[65] Breu, H., Gil, J., Kirkpatrick, D., et al., Linear time Euclidean distance transform algorithms[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1995. 17(5): pp. 529-533.
[66] Maurer Jr, C., Qi, R.,Raghavan, V., A Linear Time Algorithm for Computing Exact Euclidean Distance Transforms of Binary Images in Arbitrary Dimensions[J]. Ieee Transactions on Pattern Analysis and Machine Intelligence, 2003. 25(2): pp.265-270.
[67] Cuisenaire, O., Macq, B., Fast Euclidean distance transformation by propagation using multiple neighborhoods[J]. Computer Vision and Image Understanding, 1999. 76(2): pp. 163-172.
[68] Borgefors, G., On digital distance transforms in three dimensions[J]. Computer Vision and Image Understanding, 1996. 64(3): pp. 368-376.
[69] Bouttier, J., Di Francesco, P.,Guitter, E., Geodesic distance in planar graphs[J]. Nuclear Physics B, 2003. 663(3): pp. 535-567.
[70]唐常青,吕宏伯,黄铮等,数字形态学方法及其应用. 1990:科学出版社.
[71] Sethian, J., Level set methods and fast marching methods[J]. Journal of Computing and Information Technology, 2003. 11(1): pp. 1-2.
[72] Yushkevich, P.A., Piven, J., Hazlett, H.C., et al., User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability[J]. Neuroimage, 2006. 31(3): pp. 1116-1128.
[73] Konukoglu, E., Acar, B., Paik, D., et al., Polyp enhancing level set evolution of colon wall: Method and pilot study[J]. Medical Imaging, IEEE Transactions on, 2007. 26(12): pp. 1649-1656.
[74] Van Uitert, R.L., Bitter, I.,Summers, R.M., Detection of colon wall outer boundary and segmentation of the colon wall based on level set methods, in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2006, Ieee: NY, USA. pp. 3017-3020.
[75] Ibanez, L., Schroeder, W., Ng, L., et al., The ITK Software Guide. 2003: Kitware.
[76] VTK. Visualization Toolkit, www.vtk.org.
[77] Bitter, I., Kaufman, A.,Wax, M., Fully automatic extraction of the colon centerline and its impact on a virtual colonoscopy system[J]. Cars 2001: Computer Assisted Radiology and Surgery, 2001. 1230: pp. 625-628.
[78] Jiang, G., Gu, L., An automatic and fast centerline extraction algorithm for virtual colonoscopy, in Proceedings of 27th Annual Conference of the IEEE Engineering in Medicine and Biology(EMBC'05). 2005: Shanghai, China. pp. 5149-5152.
[79] Sadleir, R.J.T., Whelan, P.F., Colon centreline calculation for CT colonography using optimised 3D topological thinning, in First International Symposium on 3d Data Processing Visualization and Transmission. 2002: ITALY. pp. 800-803.
[80] Samara, Y., Fiebich, M., Dachman, A.H., et al., Automated calculation of the centerline of the human colon on CT images[J]. Academic Radiology, 1999. 6(6): pp. 352-359.
[81] Greenspan, H., Laifenfeld, M., Einav, S., et al., Evaluation of center-line extraction algorithms in quantitative coronary angiography[J]. IEEE Transactions on Medical Imaging, 2001. 20(9): pp. 928-941.
[82] Zhang, D., Zhao, J., Lu, L., et al., Virtual eversion and rotation of colon based on outer surface centerline[J]. Medical Physics, 2010. 37(10): pp. 5518-5529.
[83]陈维桓,微分几何. 2006,北京:北京大学出版社.
[84] Lorensen, W.,Cline, H. Marching cubes: A high resolution 3D surface construction algorithm. 1987: ACM.
[85]唐泽圣,三维数据场可视化. 1999:清华大学出版社.
[86] Tiede, U., Hoehne, K., Bomans, M., et al., Surface rendering[J]. IEEE Computer Graphics and Applications, 1990. 10(2): pp. 41-53.
[87] M Bóo, M., Amor, M., Doggett, M., et al. Hardware support for adaptive subdivision surface rendering. 2001: ACM.
[88] Vining, D., Zagoria, R., Liu, K., et al., CT cystoscopy: an innovation in bladder imaging[J]. American Journal of Roentgenology, 1996. 166(2): pp. 409-410.
[89] Shu, R., Zhou, C.,Kankanhalli, M., Adaptive marching cubes[J]. The Visual Computer, 1995. 11(4): pp. 202-217.
[90] Wu, Z.,Sullivan Jr, J., Multiple material marching cubes algorithm[J]. International Journal for Numerical Methods in Engineering, 2003. 58(2): pp. 189-207.
[91] Cline, H., Lorensen, W., Ludke, S., et al., Two algorithms for the three-dimensional reconstruction of tomograms[J]. Medical Physics, 1988. 15(3): pp. 320-327.
[92] Levoy, M., Display of surfaces from volume data[J]. Computer Graphics and Applications, IEEE, 1988. 8(3): pp. 29-37.
[93] Levoy, M., Hanrahan, P. Light field rendering. 1996: ACM.
[94] Pfister, H., Hardenbergh, J., Knittel, J., et al. The VolumePro real-time ray-casting system. 1999: ACM Press/Addison-Wesley Publishing Co.
[95] Weiler, M., Kraus, M., Merz, M., et al. Hardware-based ray casting for tetrahedral meshes. in Proceedings of the 14th IEEE Visualization 2003. 2003: IEEE Computer Society.
[96] Ray, H., Pfister, H., Silver, D., et al., Ray casting architectures for volume visualization[J]. Visualization and Computer Graphics, IEEE Transactions on, 2002. 5(3): pp. 210-223.
[97]钟灿,赵俊,张丹枫,基于GPGPU的实时结肠虚拟展平技术[J].计算机辅助设计与图形学学报, 2010.已录用.
[98] Wiki. Volume ray casting. http://en.wikipedia.org/wiki/Volume_ray_casting.
[99] Assimos, D.G., Vining, D.J., Virtual endoscopy[J]. Journal of Endourology, 2001. 15(1): pp. 47-51.
[100] Dong-Goo, K.,Jong Beom, R., A new path planning algorithm for maximizing visibility in computed tomography colonography[J]. IEEE Transactions on Medical Imaging, 2005. 24(8): pp. 957-968.
[101] Hong, L., Muraki, S., Kaufman, A., et al. Virtual voyage: Interactive navigation in the human colon. 1997: Citeseer.
[102] Fenlon, H.,Ferrucci, J., Virtual colonoscopy: What will the issues be?[J]. American Journal of Roentgenology, 1997. 169(2): pp. 453-458.