C-arm影像與電腦斷層影像之方位校準方法

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：10

、訪客IP：3.141.32.53

姓名

戴君益(Chun-Yi Tai) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

C-arm影像與電腦斷層影像之方位校準方法
(2D-3D Registration)

相關論文

★ 以擠製冷卻成型法結合相分離法製作神經再生用多孔性導管	★ 整合可調式阻力之手足復健機研究
★ 應用於肝腫瘤治療之超音波影像輔助機械臂HIFU燒灼實驗系統	★ 顱顏整型手術用植入物之設計與製作
★ 電腦輔助骨科手術用規劃及導引系統	★ 遠端遙控機械手臂腹腔鏡手術系統
★ 頭部CT與MR影像之融合	★ 手術用影像導引機械人定位及鑽孔系統
★ 機器人校正與醫學影像導引定位應用	★ 顱顏手術用規劃及導引系統
★ 醫學用超音波影像導引系統	★ 應用3D區域成長法於腦部磁共振影像之分割
★ 腦部手術用導引系統之方位校準及腦瘤影像分割	★ 超音波影像即時震波導引
★ 腫瘤偵測與顱顏骨骼重建	★ 骨科手術用C-arm影像輔助規劃及導引系統

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

本研究利用二維電腦斷層影像﹙Computed Tomography，以下簡稱為CT影像﹚、骨科手術用C-arm影像以及配合光學式定位裝置，發展一套C-arm影像與電腦斷層影像（以下均稱為2D-3D）之方位校準系統，此方位校準不需要以侵入方式取得骨骼特徵點，取而代之是使用兩張C-arm影像完成方位校準工作；此方法可配合本實驗室所發展的「椎莖骨釘植入的手術導引系統」使用，進行最小侵入式手術（Minimal Invasive Surgery），達到傷口小、復原快的手術趨勢。
本系統主要分為三個項目：1. 由CT影像分割與三維重建骨骼模型以及計算其模擬C-arm投影的數位重建X-ray影像（Digital Reconstructed Radiograph,簡稱DRR影像）；2. C-arm影像取像方位計算及其變形影像的扭正，並且計算相關空間參數；3. CT模型與C-arm影像的初始及精確方位校準。為了有效縮短由三維骨骼模型模擬C-arm投影影像的時間，本研究採用梯度演算法以及濺射成像法(Splat rendering) 篩選三維骨骼模型之體素，並由這些體素計算其DRR影像，再經過Sobel遮罩與二值化後，取得影像邊界特徵。之後利用梯度相似性演算法（Gradient Correlation - GC）取得C-arm影像特徵與DRR影像特徵的相似度後，將全域搜尋與包威爾方法相互結合進行方位校準的最佳化搜尋，完成2D-3D方位校準的最佳化。
本研究以脊椎切骨模型（Saw Bone）為範例，利用脊椎切骨模型與其DRR影像以及C-arm影像分別進行方位校準實驗，實驗結果顯示前者平均定位誤差為0.92mm，平均角度誤差為0.76°；而後者平均定位誤差為1.91mm，平均角度誤差為1.49°。

摘要(英)

This research develops a 2D-3D registration algorithm for the mapping between computed tomography (CT) generated 3D model and C-arm images. Instead of invasively measuring bone features as ICP method, the registration method needs only two C-arm images taken at different views. By integrating with CT based navigation system, this registration algorithm can be applied to minimally invasive pedicel screw insertion surgery.
The 2D-3D registration algorithm can be divided into three parts: 1. 3D CT model reconstruction and generation of digital reconstructed radiograph (DRR) image from CT images. 2. Distortion calibration and projection model formation of C-arm images; 3. Rough and fine registrations between C-arm images and 3D CT model. In order to reduce the time of DRR image generation, the algorithms of gradient projection and splat rendering are adopted to abate the number of projections of CT voxels. Then, Sobel operator is applied for acquiring boundary features of DRR and C-arm images, and the gradient correlation of boundary features of the two images is calculated to determine their similarity rate. For the sake of finding the optimal similarity of these two images, global search and Powell`s search methods are applied.
In the experiment, a saw bone model of spine is used. The average registration accuracy of location and orientation between DRR image and CT model are 0.92mm and 0.76° respectively, and the registration accuracy of location and orientation between C-arm images and CT model are 1.91mm and 1.49° respectively.

關鍵字(中)

★ 最小侵入式手術
★ 脊椎手術
★ 手術導引
★ 2D-3D方位校準

關鍵字(英)

★ Navigation System
★ 2D-3D Registration
★ Spine surgery

論文目次

摘要…………………………………………………………………..V
Abstract ……………………………………………………………………VI
目錄………………………………………………………………VIII
圖目錄……………………………………………………………………...XI
表目錄…………………………………………………………………….IXV
第一章緒論…………………………………………………………………1
1-1 研究動機………………………………………………………………..1
1-2 文獻回顧………………………………………………………………..2
1-2-1 DRR影像的處理方式………………………………………………...3
1-2-2 DRR與C-arm影像的相似性量測……………………………………4
1-2-3 方位校準的最佳化方法……………………………………………...5
1-2-4 文獻回顧總結………………………………………………………...5
1-3 研究方法簡介…………………………………………………………..6
1-4 論文介紹………………………………………………………………..7
第二章研究方法……………………………………………………………8
2-1 建構三維重建骨骼模型 ………………………………………………10
2-2 C-arm影像取得以及影像扭正………………………………………...11
2-3 初始方位校準…………………………………………………………12
2-4 建構DRR影像………………………………………………………...14
2-4-1 光束投射法………………………………………………………….14
2-4-2 梯度投影理論……………………………………………………….15
2-4-3 濺射成像法應用於DRR之產生……………………………………17
2-4-4 利用梯度投影理論與濺射成像法建構特徵DRR影像……………20
2-4-5 有限脈衝響應濾波器應用於DRR雜訊濾除………………………22
2-5 影像相似性量測………………………………………………………28
2-5-1 影像灰階值為基礎之比對方式…………………………………….28
2-5-2 以灰階資訊為基礎之比對方式…………………………………….29
2-5-3 空間資訊為基礎之比對方式……………………………………….30
2-5-4 梯度相似性演算法………………………………………………….30
2-6 方位校準方法………………………………………………………...31
第三章 C-arm影像與電腦斷層影像之方位校準方法…………………...34
3-1 系統流程………………………………………………………………34
3-2 硬體架構………………………………………………………………36
3-2-1 光學式定位裝置…………………………………………………….37
3-2-2 C-arm影像校正器………………………………………………...39
3-2-3 手術器械…………………………………………………………….40
3-3 軟體架構………………………………………………………………40
第四章實驗結果與討論…………………………………………………..47
4-1 初始方位校準誤差…………………………………………………….48
4-1-1 DRR影像方位校準之初始方位校準誤差分析…………………….48
4-1-2 C-arm 影像方位校準之初始方位校準誤差分析…………………..51
4-1-3 初始方位校準實驗結果討論……………………………………..53
4-2全域搜尋法參數決定實驗……………………………………………..54
4-3 方位校準實驗…………………………………………………………59
4-4 DRR影像與迭代時間關係測試實驗………………………………..63
4-5 誤差分析………………………………………………………………66
4-5-1 光學式定位誤差…………………………………………………….66
4-5-2 C-arm影像校正以及相關參數誤差………………………………...66
4-5-3 電腦斷層掃瞄影像重建三維模型誤差…………………………….69
4-5-4 DRR影像與C-arm影像之相似性比對誤差………………………..69
4-5-5 方位校準誤差……………………………………………………….69
4-5-6 人為操作誤差……………………………………………………….70
第五章結論………………………………………………………………..71
參考文獻……………………………………………………………………73

參考文獻

[1] Assaker, R., "Minimal access spinal technologies: state-of-the-art, indications, and techniques.", Joint Bone Spine 71, pp. 459-469, 2004.
[2] Benameur, S., Mignotte, M., Parent, S., Labelle, H., Skalli, W., DeGuise,J.A., "3D/2D Registration and Segmentation of Scoliotic Vertebrae Using Statistical Models.", Computerized Medical Imaging and Graphics, 27(5), pp. 321-337, 2003.
[3] Besl, P. J., Mckay, N. D., “A method for registration of 3-D shapes,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 14, No.2, pp. 239-256, 1992.
[4] Birkfellner, W., Seemann, R., Figl, M., Hummel, J., Ede, C., Homolka, P., Yang, X., Niederer, P., Bergmann, H., “Wobbled Splatting – a fast perspective volume rendering method for simulation of X-ray images from CT”, Phys Med Biol 50(9), pp. N73-N84, 2005.
[5] Birkfellner, W., Wirth, J., Burgstaller, W., Baumann, B., Staedele, H., Hammer, B., Gellrich, N. C., Jacob, A. L., Regazzoni, P., Messmer, P.,“A faster method for 3D/2D medical image registration—A simulation study,” Phys. Med. Biol., vol. 48, pp. 2665-2679, 2003.
[6] BrainLAB, Germany, “BrainLabs Fluoro to CT Registration” http://www.brainlab.com/
[7] Cyr C. M., Kamal, A. F., Sebastian, T. B., Kimia, B. B.,"2D-3D registration based on shape matching", Proc. IEEE Workshop on Mathematical Methods in Biomedical Image Analysis., pp. 198-203, 2000.
[8] DiGioia, A.M., Plakeychuk, A.Y., Levison, T.J., Jaramaz, B.,” Mini-incision technique for total hip arthroplasty with navigation.”, J Arthroplasty.; vol. 18, no.2 , pp. 123-128, 2003.
[9] Frederik, M., Andre, C., Dirk, V., “Multi-modality image registration by maximization of mutual information.”, IEEE Trans Med Imag, Vol. 16(2), pp. 187-198, 1997.
[10] Gebhard, F., Weidner, A., Liener, U. C., Stöckle, U., and Arand, M., “Navigation at the spine,” Injury, Int. J. Care Injured, Vol. 35, pp. S-A35-45, 2004.
[11] Gueziec, A., Wu, K., Kalvin, A., Williamson, B., Kazanzides, Vorhis, P., "Providing Visual Information to Validate 2-D to 3-D Registration.", Medical Image Analysis, Vol. 4, No.4, pp. 357-374, 2004.
[12] Horn, BKP., "Closed-form solution of absolute orientation using unit quaternions", Journal of the Optical Society of America. A, Optics and image science, Vol. 4, No.4, pp. 629-642, April 1987.
[13] Jando, V.T., Duncan, C.P., “Two-incision technique for minimally invasive total hip arthroplasty.”, Operative Techniques in Orthopaedics., Vol. 14, Issue. 2, pp. 102-110, 2004.
[14] Joskowicz, L., Knaan, D., "How to achieve fast, accurate, and robust rigid registration between fluoroscopic X-ray and CT images," CARS 2004., pp. 147-152. Chicago, IL, USA, 2004.
[15] LaRose, D., Bayouth, J., and Kanade, T. , “Transgraph: Interactive intensity-based 2D/3D registration of X-ray and CT data,” Proc. SPIE-Medical Imaging 2000: Image Processing, Vol. 3979, pp. 385-396, San Diego, CA, USA, 2000.
[16] Leventon, M.E., WellsIII, W.M., Grimson W. E. L., "Multiple View 2D-3D Mutual Information Registration," Proc. Image Understanding Workshop., pp. 625-630. New Orleans, LA, USA, 1997.
[17] Livyatan, H., Yaniv, Z., Joskowicz, L., “Gradient-based 2-D/3-D rigid registration of fluoroscopic X-ray to CT,” IEEE Trans. Med. Imag., vol. 22, no. 11, pp. 1395-1406, Nov. 2003.
[18] Lorensen, W. E., Cline, H. E., “Marching cubes: a high resolution 3D surf ace construction algorithm,” Computer Graphics, Vol. 21, No. 4, pp. 163-169, 1987.
[19] Maintz A. J. B, "An Overview of Medical Image Registration Methods", Imaging Science Department, Imaging Center. Vtrecht, IEEE Transactions on Image Processing, vol., no., page, 2002.
[20] Medtronic Inc., “FluoroMerge® Software Fluoroscopy/CT data merge software, ” U.S.A., http://www.stealthstation.com/index.jsp
[21] Mukundan, R., Ong, S., H, Lee., “Image analysis by Tchebichef moments,” IEEE Trans Image Processing, Vol. 10. No. 9, pp. 1357-1364, 2001.
[22] Press, W. H., Flannery, B. P., Teukolsky, S.A., W.T. Vetterling, "Numerical Recipes in C: The Art of Scientific Computing", ISBN-0-521-43108-5, Cambridge University Press, UK, 1988.
[23] Roth, M., Dötter, M., Burgkart, R., A. Schweikard, "Fast intensity-based fluoroscopy-to-CT registration using pattern search optimization," CARS 2004., pp. 165-170, Chicago, IL, USA, 2004.
[24] Russakoff, D. B., Rohlfing, T., Adler, J. R. Jr, and Maurer, C. R. Jr, “Intensity-based 2D-3D spine image registration incorporating a single fiducial marker,” Acad Radiol, Vol. 12, pp. 37-50, 2004.
[25] Russakoff, D. B., Rohlfing, T., Mori, K., Rueckert, D., Ho, A., Adler, J.R., Maurer, C.R., "Fast Generation of Digitally Reconstructed Radiographs Using Attenuation Fields With Application to 2D-3D Image Registration.", IEEE Transactions On Medical Imaging., Vol. 24, no. 4, pp. 1441-1454, 2005.
[26] Stephen, B., et al., "Technique of Tissue-Preserving, Minimally- Invasive Total Hip Arthroplasty using a Superior Capsulotomy.", Operative Techniques in Orthopedics, Vol. 14, No. 2, pp. 94-101, 2004.
[27] Weese, J., Gocke, R., Penney, G.P., Desmedt, P., Buzug, T. M., Schumann, H., "Fast voxel-based 2D/3D registration algorithm using a volume rendering method based on the shear-warp factorization," SPIE International Symposium on Medical Imaging., San Diego, CA, USA, 1999.
[28] Wein, W., “Intensity based rigid 2D-3D registration algorithms for radiation therapy”, Master’s thesis, Technische Universitat Munchen, German, Dec. 2003.
[29] Wein, W., Roeper, B., Navab, N., "2D/3D Registration Based on Volume Gradients," SPIE International Symposium on Medical Imaging, pp. 144-150, San Diego, CA, USA, 2005.
[30] Yang, X., Birkfellner, W., Niederer, P., “A similarity measure based on Tchebichef moments for 2D/3D medical image registration.” CARS 2004., pp. 153-158, Chicago, IL, USA, 2004.
[31] Zollei, L., Grimson, E., Norbash, A., Wells, W., "2D-3D Rigid Registration of X-Ray Fluoroscopy and CT Images Using Mutual Information and Sparsely Sampled Histogram Estimators," IEEE Computer Society Conference on Computer Vision and Pattern Recognition., Vol 2, pp. 696-703, MIT, Cambridge, MA, USA, 2001.
[32] 夏笙, “應用於椎莖骨釘植入的手術導引系統”, 碩士論文, 中央大學機械工程研究所, 2005.
[33] 楊遠祥, “應用於股骨轉子間骨折回復手術之C-arm based 手術導引系統”, 碩士論文, 中央大學機械工程研究所, 2005.

指導教授

曾清秀(Ching-Shiow Tseng)

審核日期

2007-1-22

推文