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研究生:林揚毅
研究生(外文):LIN, YANG-YI
論文名稱:踝關節統計形狀模擬以利少數平面X光影像重建個人化骨骼幾何
論文名稱(外文):Statistical Shape Modeling of the Ankle for Subject-Specific Bony Geometry Reconstruction from Plane Radiographs
指導教授:林正忠林正忠引用關係
指導教授(外文):LIN, CHENG-CHUNG
口試委員:呂東武許維君林正忠
口試委員(外文):LU, TUNG-WUHSU, WEI-CHUNLIN, CHENG-CHUNG
口試日期:2020-02-14
學位類別:碩士
校院名稱:輔仁大學
系所名稱:電機工程學系碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:66
中文關鍵詞:重建統計形狀模型X光踝關節
外文關鍵詞:reconstructionstatistical shape modelX-rayankle
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本研究旨在提出並評估使用統計形狀模型,重建受試者個人化踝關節的可行性。本研究從27名年輕健康志願者蒐集其踝關節三維影像作為訓練模型以建立統計形狀模型。使用“增生和自行適應網格”演算法,對所有已對齊之形狀模型進行預先處理,產生具有相應性且一致數量界標的訓練模型,這些訓練模型將進行主成分分析以獲取踝關節每一塊骨骼形狀的所有變化模式。將分析出的特徵值和特徵向量作為形狀參數,變更其加權值以決定模型之形狀變異量。形狀變異量和平均形狀共同組成統計形狀模型。為獲取踝關節模型之形狀並計算模型在空間中的擺位,本研究開發了一種雙層式優化方法(基因演算法)來更新擺位和形狀參數,直到模型之數位重建透視影像與6個視野的X光透視影像達到最佳匹配。外層優化旨在搜索前3筆最佳形狀參數,而內層優化計算6個位置參數的最佳組合。為評估提出流程之可行性,進行計算機模擬,以志願者脛骨、距骨與跟骨之電腦斷層掃描影像生成六個視野的虛擬X光透視影像。將本方法重建的踝關節模型,與作為參照標準的受試者電腦斷層影像進行比較,得出脛骨的平均(標準偏差)重建誤差為0.59(0.05)mm,距骨為0.94(0.08)mm,跟骨為0.84(0.09)mm。本研究提出一種新的雙層優化方案,經測試能以高精確度重建個人踝關節骨骼模型。
This purpose of the study was to propose and evaluate the feasibility of an optimization procedure to customize subject-specific ankle joint complex (AJC) using a statistical shape model (SSM). The SSM model was established by utilizing a number of training shape models obtained from 27 young healthy volunteers. All the shape models were preprocessed to have a corresponding and a consistent number of landmarks using the “growing and adaptive meshes” algorithm. Principle component analysis for these best-aligned training shape model sets gave the primary modes of shape variations. Taking the shape parameters as weighting factors, a linear combination of shape variations gave the overall variation of the shape. Summation of the overall variation and mean shape determined the SSM. To customize the shape of the AJC models and determine the 3-D pose of the model, we developed a dual-level heuristic optimization scheme (genetic algorithm) to update pose and shape parameters until the model-projected digitally reconstructed radiographs (DRRs) best-matched to 6 view x-ray images. The outer-level optimization aimed to search for an optimal set of first 10 shape parameters while the inner-level optimization was operated to determine an optimal set of 6 pose parameters. To evaluate the feasibility of the proposed optimization pipeline, a computer simulation study was carried out. Six view x-ray images were generated by virtually projecting a volunteer’s CT volumes of the tibia, talus, and calcaneus. The reconstructed AJC models using the proposed method were compared with the subject-specific CT-derived surface bone models, giving mean (standard deviation) shape errors of 0.59(0.05) mm for the tibia, 0.94(0.08) mm for the talus and 0.84(0.09) mm for the calcaneus. In conclusion, the study proposed a new dual-level optimization scheme to customize the subject-specific AJC shape models. The accuracy of the reconstructed shape models showed that the proposed method may provide a feasible approach to customize the individual AJC models.
摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1 研究背景 1
1.2 踝關節解剖構造 2
1.3 統計形狀模型 3
1.4 文獻回顧 4
1.5 研究目的 6
第二章 材料與方法 7
2.1 試體來源 8
2.2 受試者骨骼模型 9
2.3 統計形狀模型 10
2.4 模型對應關係(Shape Correspondence) 12
2.5 建構模型結構變異(Shape Variation) 16
2.6 電腦模擬 18
2.7 重建立體關節幾何結構 21
2.8 重建誤差之量化 26
第三章 模擬結果 28
3.1 對齊方式對建立模型對應點之影響 28
3.2 統計形狀模型 30
3.3 不同視野數量下的成品比較 33
3.4 位置誤差 41
3.5 角度誤差 47
3.6 不同骨頭之重建誤差 53
3.7 在已知骨骼擺位下進行重建 54
第四章 討論 56
4.1 對齊方式對於建立模型對應點之影響 58
4.2 視野數量對於重建誤差之影響 58
4.3 不同骨頭之位置誤差 58
4.4 不同骨頭之角度誤差 59
4.5 不同骨頭之重建誤差 60
4.6 由位置誤差造成重建形狀誤差 60
4.7 研究限制 61
第五章 結論 62
參考文獻 63

[1] P. Gamage, S. Q. Xie, P. Delmas, and P. Xu, "3D reconstruction of patient specific bone models from 2D radiographs for image guided orthopedic surgery," in Proc. Digital Image Computing: Techniques and Applications, Melbourne, Australia, pp. 212-216, 2009.
[2]J. Fernandez, P. Mithraratne, S. Thrupp, M. Tawhai, and P. Hunter, "Anatomically based geometric modelling of the musculo-skeletal system and other organs," Biomechanics and Modeling in Mechanobiology, vol. 2, no. 3, pp. 139-155, 2004.
[3]P. R. Krekel, C. P. Botha, E. R. Valstar, P. W. de Bruin, P. M. Rozing, and F. H. Post, "Interactive simulation and comparative visualisation of the bone-determined range of motion of the human shoulder," in Proc. SimVis, Magdeburg, Germany, pp. 275-288, 2006.
[4]R. A. Siston, N. J. Giori, S. B. Goodman, and S. L. Delp, "Surgical navigation for total knee arthroplasty: a perspective," Journal of Biomechanics, vol. 40, no. 4, pp. 728-735, 2007.
[5]O. L. Harrysson, Y. A. Hosni, and J. F. Nayfeh, "Custom-designed orthopedic implants evaluated using finite element analysis of patient-specific computed tomography data: femoral-component case study," BMC Musculoskeletal Disorders, vol. 8, no. 1, pp. 91-100, 2007.
[6]E. Stindel, J. K. Udupa, B. E. Hirsch, and D. Odhner, "An in vivo analysis of the motion of the peri-talar joint complex based on MR imaging," IEEE Transactions on Biomedical Engineering, vol. 48, no. 2, pp. 236-247, 2001.
[7]A. M. Caputo et al., "In vivo kinematics of the tibiotalar joint after lateral ankle instability," The American Journal of Sports Medicine, vol. 37, no. 11, pp. 2241-2248, 2009.
[8]K. Imai et al., "In vivo three-dimensional analysis of hindfoot kinematics," Foot & Ankle International, vol. 30, no. 11, pp. 1094-1100, 2009.
[9]K. Rathnayaka, T. Sahama, M. A. Schuetz, and B. Schmutz, "Effects of CT image segmentation methods on the accuracy of long bone 3D reconstructions," Medical Engineering & Physics, vol. 33, no. 2, pp. 226-233, 2011.
[10]P. T. Liu, W. P. Pavlicek, M. B. Peter, M. J. Spangehl, C. C. Roberts, and R. G. Paden, "Metal artifact reduction image reconstruction algorithm for CT of implanted metal orthopedic devices: a work in progress," Skeletal Radiology, vol. 38, no. 8, pp. 797-802, 2009.
[11]J. Y. Huang et al., "An evaluation of three commercially available metal artifact reduction methods for CT imaging," Physics in Medicine & Biology, vol. 60, no. 3, pp. 1047-1067, 2015.
[12]L. Caponetti and A. M. Fanelli, "Computer-aided simulation for bone surgery," IEEE Computer Graphics and Applications, vol. 13, no. 6, pp. 86-92, 1993.
[13]P. Gamage, S. Q. Xie, P. Delmas, and W. L. Xu, "Diagnostic radiograph based 3D bone reconstruction framework: Application to the femur," Computerized Medical Imaging and Graphics, vol. 35, no. 6, pp. 427-437, 2011.
[14]A. Le Bras et al., "3D reconstruction of the proximal femur with low-dose digital stereoradiography," Computer Aided Surgery, vol. 9, no. 3, pp. 51-57, 2004.
[15]A. Baudoin, W. Skalli, J. A. de Guise, and D. Mitton, "Parametric subject-specific model for in vivo 3D reconstruction using bi-planar X-rays: application to the upper femoral extremity," Medical & Biological Engineering & Computing, vol. 46, no. 8, pp. 799-805, 2008.
[16]N. Sarkalkan, H. Weinans, and A. A. Zadpoor, "Statistical shape and appearance models of bones," Bone, vol. 60, pp. 129-140, 2014.
[17]N. Baka et al., "2D–3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models," Medical Image Analysis, vol. 15, no. 6, pp. 840-850, 2011.
[18]V. Karade and B. Ravi, "3D femur model reconstruction from biplane X-ray images: a novel method based on Laplacian surface deformation," International Journal of Computer Assisted Radiology and Surgery, vol. 10, no. 4, pp. 473-485, 2015.
[19]T.-Y. Tsai, J.-S. Li, S. Wang, P. Li, Y.-M. Kwon, and G. Li, "Principal component analysis in construction of 3D human knee joint models using a statistical shape model method," Computer Methods in Biomechanics and Biomedical Engineering, vol. 18, no. 7, pp. 721-729, 2015.
[20]G. Zheng and S. Schumann, "3D reconstruction of a patient‐specific surface model of the proximal femur from calibrated x‐ray radiographs: a validation study," Medical physics, vol. 36, no. 4, pp. 1155-1166, 2009.
[21]T. Heimann et al., "Comparison and evaluation of methods for liver segmentation from CT datasets," IEEE Transactions on Medical Imaging, vol. 28, no. 8, pp. 1251-1265, 2009.
[22]T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, "Active shape models-their training and application," Computer Vision And Image Understanding, vol. 61, no. 1, pp. 38-59, 1995.
[23]V. Blanz and T. Vetter, "A morphable model for the synthesis of 3D faces,” in Proc. Siggraph, vol. 99, no. 1999, pp. 187-194, 1999.
[24]S. Wold, K. Esbensen, and P. Geladi, "Principal component analysis," Chemometrics and Intelligent Laboratory Systems, vol. 2, no. 1-3, pp. 37-52, 1987.

[25]N. Baka et al., "Statistical shape model-based femur kinematics from biplane fluoroscopy," IEEE Transactions on Medical Imaging, vol. 31, no. 8, pp. 1573-1583, 2012.
[26]A. Hurvitz and L. Joskowicz, "Registration of a CT-like atlas to fluoroscopic X-ray images using intensity correspondences," International Journal of Computer Assisted radiology and Surgery, vol. 3, no. 6, pp. 493-504, 2008.
[27]J. Yao, "Assessing accuracy factors in deformable 2D/3D medical image registration using a statistical pelvis model," in Proc. Ninth IEEE International Conference on Computer Vision, pp. 1329-1334, 2003.
[28]C.-C. Lin, T.-W. Lu, T.-M. Wang, C.-Y. Hsu, and T.-F. Shih, "Comparisons of surface vs. volumetric model-based registration methods using single-plane vs. bi-plane fluoroscopy in measuring spinal kinematics," Medical Engineering & Physics, vol. 36, no. 2, pp. 267-274, 2014.
[29]M. Fleute and S. Lavallée, "Nonrigid 3-D/2-D registration of images using statistical models,” in Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 138-147, 1999.
[30]H. Lamecker, T. H. Wenckebach, and H.-C. Hege, "Atlas-based 3D-shape reconstruction from X-ray images,” in Proc. International Conference on Pattern Recognition (ICPR'06), vol. 1, pp. 371-374, 2006.
[31]G. Zheng, M. Á. Ballester, M. Styner, and L.-P. Nolte, "Reconstruction of patient-specific 3D bone surface from 2D calibrated fluoroscopic images and point distribution model,” in Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 25-32, 2006.
[32]S. Benameur, M. Mignotte, S. Parent, H. Labelle, W. Skalli, and J. de Guise, "3D/2D registration and segmentation of scoliotic vertebrae using statistical models," Computerized Medical Imaging and Graphics, vol. 27, no. 5, pp. 321-337, 2003.
[33]S. Laporte, W. Skalli, J. De Guise, F. Lavaste, and D. Mitton, "A biplanar reconstruction method based on 2D and 3D contours: application to the distal femur," Computer Methods in Biomechanics & Biomedical Engineering, vol. 6, no. 1, pp. 1-6, 2003.
[34]G. Zheng, S. Gollmer, S. Schumann, X. Dong, T. Feilkas, and M. A. G. Ballester, "A 2D/3D correspondence building method for reconstruction of a patient-specific 3D bone surface model using point distribution models and calibrated X-ray images," Medical Image Analysis, vol. 13, no. 6, pp. 883-899, 2009.
[35]T. S. Tang and R. E. Ellis, "2D/3D deformable registration using a hybrid atlas," in Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 223-230, 2005.
[36]盧欣怡, 「利用雙平面動態 X 光量測外側韌帶不穩定患者在功能性動作下踝關節之三維運動」,碩士論文,國立臺灣大學,臺北,2018。
[37]W. E. Lorensen and H. E. Cline, "Marching cubes: A high resolution 3D surface construction algorithm," in Proc. ACM Siggraph Computer Graphics, vol. 21, no. 4, pp. 163-169, 1987.
[38]L. Ferrarini, H. Olofsen, W. M. Palm, M. A. Van Buchem, J. H. Reiber, and F. Admiraal-Behloul, "GAMEs: growing and adaptive meshes for fully automatic shape modeling and analysis," Medical Image Analysis, vol. 11, no. 3, pp. 302-314, 2007.
[39]S. Marsland, J. Shapiro, and U. Nehmzow, "A self-organising network that grows when required," Neural Networks, vol. 15, no. 8-9, pp. 1041-1058, 2002.
[40]P. J. Besl and N. D. McKay, "Method for registration of 3-D shapes," in Proc. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, pp. 239-256, 1992.
[41]I. Söderkvist and P.-Å. Wedin, "Determining the movements of the skeleton using well-configured markers," Journal of Biomechanics, vol. 26, no. 12, pp. 1473-1477, 1993.
[42]T. F. Cootes and C. J. Taylor, "Statistical models of appearance for computer vision," Manchester University of Imaging Science and Biomedical Engineering , Manchester, 2004.
[43]T. Heimann and H.-P. Meinzer, "Statistical shape models for 3D medical image segmentation: a review," Medical Image Analysis, vol. 13, no. 4, pp. 543-563, 2009.
[44]C. Spoor and F. Veldpaus, "Rigid body motion calculated from spatial co-ordinates of markers," Journal of Biomechanics, vol. 13, no. 4, pp. 391-393, 1980.
[45]李松穎, 「發展人體膝關節統計形狀模型以利三維動態 X 光量測關節運動」 ,碩士論文,國立臺灣大學,臺北,2018。
[46]G. P. Penney, J. Weese, J. A. Little, P. Desmedt, and D. L. Hill, "A comparison of similarity measures for use in 2-D-3-D medical image registration," IEEE Transactions on Medical Imaging, vol. 17, no. 4, pp. 586-595, 1998.
[47]Y. Chaibi et al., "Fast 3D reconstruction of the lower limb using a parametric model and statistical inferences and clinical measurements calculation from biplanar X-rays," Computer Methods in Biomechanics and Biomedical Engineering, vol. 15, no. 5, pp. 457-466, 2012.

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