| 研究生: |
黃書毅 Huang, Shu-Yi |
|---|---|
| 論文名稱: |
基於多姿勢電腦斷層掃描影像之大拇指基關節運動量化分析 Quantitative Motion Analysis of Trapeziometacarpal Joint by Using Multi-Posture Computer Tomography Images |
| 指導教授: |
孫永年
Sun, Yung-Nien |
| 學位類別: |
碩士 Master |
| 系所名稱: |
電機資訊學院 - 資訊工程學系 Department of Computer Science and Information Engineering |
| 論文出版年: | 2013 |
| 畢業學年度: | 101 |
| 語文別: | 中文 |
| 論文頁數: | 57 |
| 中文關鍵詞: | 大拇指基關節 、自由度 、電腦斷層掃描影像 |
| 外文關鍵詞: | Trapeziometacarpal Joint, Degree of Freedom, Computer Tomography Images |
| 相關次數: | 點閱:105 下載:1 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
手是人們使用相當頻繁的器官之一,所以手部關節功能容易退化或產生病變,造成患者之不便。尤其大拇指基關節(Trapeziometacarpal Joint, TMC Joint)是經常發生病變的部位。拇指基關節病變主要是在大拇指根部,也就是大拇指連接到手腕的第一個關節。與其他手指比較起來,大拇指具有較為特殊的解剖構造,具有較高的運動自由度。大拇指基關節是手部活動度及活動範圍最大的關節,它具有伸彎、內收外展及轉動的功能。
三維(Three-dimensional, 3D)的醫學成像系統在結構解析度的呈現上較好的兩種分別為核磁共振影像(MRI)與電腦斷層掃描(CT)。由於我們著重於關節骨塊的影像分析,故利用電腦斷層掃描對於硬組織具有良好解像力之特性,拍攝運動序列中的幾個姿勢,再利用此大拇指基關節多個姿勢的電腦斷層掃描影像進行關節結構運動狀態的重建以及關節面生物力學參數的量測與分析。整體來說,本研究包含四項重點,分別為骨塊自動分割、建構關節模型、計算關節運動參數、與關節參數及運動視覺化,以進行同受測者(Intra-subject)與不同受測者(Inter-subject)之相關關節運動分析。研究成果將對大拇指基關節的生物力學探討有所幫助,也能協助人工關節製作與評估關節置換手術之成效。
Hand is one of the most used organs in our daily life. Due to the frequent use, the joint function of hand is easy to degrade and produce lesion, especially thumb’s Trapeziometacarpal joint (TMC joint). The pathology of TMC joint occurs at the base of thumb, i.e. the first joint between thumb and wrist. Compared to other fingers, thumb has a unique anatomical structure which has more degrees of freedom of motion. Basically the structure of hand is chain linked conformation with the characteristics of different finger joint having different degree of freedom of motion. Among all joints, TMC joint is the joint with the maximum degrees of freedom of motion and therefore has wider range of motion including adduction, abduction, extension, flexion and rotation.
Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) have better anatomical resolution than other 3D medical imaging systems. Due to the high image resolution for bone tissue, CT was used to scan multiple postures of thumb motion for each subject. Then, the CT images were used to reconstruct 3D TMC joint model for each motion posture of thumb for each subject. Meanwhile, the bio-mechanics parameters of contact surface of TMC joint were calculated.
Overall, there are four major contributions in this study: automatic bone segmentation, 3D bone model construction, joint parameter calculation and bone parameter visualization for intra-subject and inter-subject analyses. The results of this study can provide not only valuable information for TMC joint bio-mechanics study but also supply joint contact surface information for artificial joint manufacture and evaluate effectiveness of artificial joint replacement surgery.
[1]http://uwmsk.org/RadAnat/HandPALabelled.html
[2]J. H. Youngleson, “The Management of the Contracted First Web Space,” S. A. Medical Journal, vol. 39, no. 32, pp. 716?719, 1965.
[3]J. M. Moran, J. H. Hemann, and A. S. Greenwald, “Finger Joint Contact Areas and Pressures,” Journal of Orthopaedic Research, vol. 3, no. 1, pp. 49-55, 1985.
[4]L. J. Soslowsky, E. L. Flatow, L. U. Bigliani, R. J. Pawluk, G. A. Ateshian, and V. C. Mow, “Quantitation of In Situ Contact Areas at the Glenohumeral Joint: A Biomechanical Study,” Journal of Orthopaedic Research, pp. 524-534, 1992.
[5]G. Windisch, B. Odehnal, R. Reimann, F. Anderhuber, H. Stachel, “Contact Areas of the Tibiotalar Joint,” Journal of Orthopaedic Research, pp. 1481-1487, Nov. 2007.
[6]G. A. Ateshian, J. W. Ark, M. P. Rosenwasser, R. J. Pawluk, L. J. Soslowsky, and V. C. Mow, “Contact Areas in the Thumb Carpometacarpal Joint,” Journal of Orthopaedic Research, pp. 450-458, 1995.
[7]L.C. Kuo, W.P. Cooney III, K.N. An, K.Y. Lai, S.M. Wang, and F.C. Su, “Effects of Age and Gender on the Movement Workspace of the Trapeziometacarpal Joint,” Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, vol. 223, no. 2, pp. 133-142, Feb. 2009.
[8]L.C. Kuo, W.P. Cooney III., K.R. Kaufman, Q.S. Chen, F.C. Su, and K.N. An, “A quantitative method to measure maximal workspace of the trapeziometacarpal joint-normal model development,” Journal of Orthopaedic Research, pp. 600-606, 2004.
[9]L. Y. Chang and N. S. Pollard, “Method for Determining Kinematic Parameters of the In Vivo Thumb Carpometacarpal Joint,” IEEE Trans. Biomedical Engineering, vol. 55, no. 7, pp. 1897-1906, 2008.
[10]R. P. Robinson, P. T. Simonian, I. M. Gradisar, and R. P. Ching, “Joint Motion and Surface Contact Area Related to Component Position in Total Hip Arthroplasty,” Journal of Bone and Joint Surgery, pp. 140-146, 1997.
[11]J. H. Brechter, C. M. Powers, M. R. Terk, S. R. Ward, and T. Q. Lee, “Quantification of Patellofemoral Joint Contact Area Using Magnetic Resonance Imaging,” Magnetic Resonance Imaging, pp. 955–959, 2003.
[12]T. F. Besier, G. E. Gold, G. S. Beaupre, and S. L. Delp, “A Modeling Framework to Estimate Patellofemoral Joint Cartilage Stress In Vivo,” Medicine & Science in Sports & Exercise, vol. 37, no. 11, pp. 1924-1930, Nov. 2005.
[13]P. Cerveri, E. De Momi, M. Marchente, G. Baud-Bovy, P. Scifo, R.M.L. Barros, and G. Ferrigno, “Method for the Estimation of A Double Hinge Kinematic Model for the Trapeziometacarpal Joint Using MR Imaging,” Computer Methods Biomechanics Biomedical Engineering, vol. 13, no. 3, pp. 387-396, Jun. 2010.
[14]J. G. Snel, H. W. Venema, T. M. Moojen, M. J. P. F. Ritt, C. A. Grimbergen, and G. J. den Heeten, “Quantitative in vivo analysis of the kinematics of carpal bones from three-dimensional CT images using a deformable surface model and a three-dimensional matching technique,” Medical Physics, vol. 27, no. 9, pp. 2037-2047, 2000.
[15]M. Mancas, B. Gosselin, and B. Macq, ”Segmentation Using a Region Growing Thresholding,” In Proceedings of Image Processing: Algorithms and Systems, pp. 388-398, 2006.
[16]R. Pohle and K. D. Toennies, “Segmentation of Medical Images Using Adaptive Region Growing,” Proceedings of SPIE, vol. 4322, pp. 1337-1346, 2001.
[17]T. F. Chan and L. A. Vese, ”Active Contours Without Edges,” IEEE Trans. on Image Processing, vol. 10, no. 2, pp. 266-277, Feb. 2001.
[18]S. H. Lee and J. K. Seo, “Level set-based bimodal segmentation with stationary global minimum”, IEEE Trans. on Image Processing, vol. 15, no. 9, pp. 2843-2852, Sep. 2006.
[19]L. A. Vese and T. F. Chan, “A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model," International Journal of Computer Vision, vol. 50, no. 3, pp. 271-293, 2002.
[20]M. Kass, A. Witkin, and D. Terzoulos, “Snake: Active contour models,” International Journal of Computer Vision, vol. 1, no. 4, pp. 321-331, 1987.
[21]C. Xu and J. L. Prince, “Snake, shapes, and gradient vector flow,” IEEE Trans. on Image Processing, vol. 7, no. 3, pp. 359-369, Mar. 1998.
[22]J. G. Snel, H. W. Venema, and C. A. Grimbergen, “Deformable Triangular Surfaces Using Fast 1-D Radial Lagrangian Dynamics—Segmentation of 3-D MR and CT Images of the Wrist,” IEEE Trans. on Medical Imaging, vol. 21, no. 8, pp. 888-903, 2002.
[23]A. Yezzi, L. Zo‥llei, and T. Kapur, “A Variational Framework for Integrating Segmentation and Registration through Active Contours,” Medical Image Analysis, vol. 7, no. 2, pp. 171-185, 2003.
[24]V. Zagrodsky, V. Walimbe, C. R. Castro-Pareja, J. X. Qin, J. M. Song, and R. Shekhar, ” Registration-Assisted Segmentation of Real-Time 3-D Echocardiographic Data Using Deformable Models,” IEEE Trans. on Medical Imaging, vol. 24, no. 9, pp. 1089-1099, Sep. 2005.
[25]H. C. Chen, I. M. Jou, C. K. Wang, F. C. Su, and Y. N. Sun, “Registration-based segmentation with articulated model from multipostural magnetic resonance images for hand bone motion animation,” Medical Physics, nol. 37, no. 6, pp. 2670-2682, 2010.
[26]H. C. Chen, C. J. Lin, C. H. Wu, C. K. Wang, and Y. N. Sun, “Automatic insall-salvati ratio measurement on lateral knee x-ray images using model-guided landmark localization,” Physics in Medicine and Biology, vol. 55, no. 22, pp. 6785-6800, 2010.
[27]M. R. Kaus, V. Pekar, C. Lorenz, R. Truyen, S. Lobregt, and J. Weese, “Automated 3-D PDM construction from segmented images using deformable models,” IEEE Trans. on Medical Imaging, vol. 22, no. 8, pp. 1005-1013, 2003.
[28]G. E. Marai, C. M. Grimm, and D. H. Laidlaw, “Arthrodial Joint Markerless Cross-Parameterization and Biomechanical Visualization,” IEEE Trans. on Visualization and Computer Graphics, vol. 13, no. 5, pp. 1095-1103, 2007.
[29]J. Fripp, S. Ourselin, S. Warfield, A. Mewes, and S. Crozier, “Automatic Generation of 3D Statistical Shape Models of the Knee Bones,” APRS Workshop on Digital Image Computing, pp. 15-21, 2005.
[30]R. H. Davies, C. J. Twining, T. F. Cootes, and C. J. Taylor, “Building 3-D statistical shape model by direct optimization,” IEEE Trans. on Medical Imaging, vol. 29, no. 4, pp. 961-981, 2010.
[31]M. Levoy, ”Display of Surfaces from Volume Data,” IEEE Computer Graphics and Applications, vol. 8, no. 3, pp. 29-37, May. 1988.
[32]W. E. Lorensen and H. E. Cline, “Marching Cubes: A High Resolution 3D Surface Construction Algorithm,” Computer Graphics, vol. 21, no. 4, pp. 163-169, July. 1987.
[33]G. P. Chen, “In Vivo Study of Trapeziometacarpal Joint Gliding and Contact Using 3D Images,” Master Thesis, July. 2011.
[34]W. H. Press, S. A. Teukolsky, W. T. Vetterling, B. P. Flannery, "Numerical Recipes in C, 2nd ed," Cambridge: Cambridge University Press, 1992.