膝關節局部表面重建研究

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姓名

蘇文(Vincent So) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

膝關節局部表面重建研究
(A research on surface reconstruction for partial knee surface repairing)

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摘要(中)

在膝關節表面重建手術中，由於所裝配的植入物表面直接與病患的半月板及關節軟骨接觸摩擦，若此一表面與膝關節未受損處之表面無法正確吻合，則可能導致病患膝關節的損傷及加速植入物的損壞。為了避免上述問題，本研究希望藉由膝關節上受損部位周圍之軟骨表面資料，重建受損部位應有的平滑表面，以做為植入物設計的參考。
在表面重建的方法上，本研究提出以移動式最小平方法和徑向基底函數神經網路作為修補膝關節表面缺損的方法。前者是在常見的最小平方法中加入權重值做調整，以修正嵌合的誤差；後者則是利用所建構之徑向基底函數，以函數逼近的方式找到輸入與輸出間的關係，藉此建立膝關節表面的數學模型。
由實驗結果可以發現徑向基底函數神經網路較適合應用於修補膝關節表面上的缺損，在膝關節較易受損的內髁部位，即使受損部份佔重建區域達49.7％，其修補的最大誤差僅為0.171㎜、平均誤差亦僅為0.052㎜。故日後在設計用於修補膝關節表面微小受損的植入物時，可考慮採用以徑向基底函數神經網路計算所得之曲面作為植入物設計的依據。

摘要(英)

In resurfacing reconstruction of the knee joint, the surface of the implant will contact the plateau and joint cartilage dynamically. If the implant surface does not fit well with the joint surface, it could lead to the damage of both the knee joint and the implant.
This research aims to use the surrounding cartilage surface of the joint defect to reconstruct original surface, which will be used for the design of the resurfacing implant.
As for the methods of surface reconstruction, moving least squares method and radial basis function neural network are proposed for restoration of the normal surface of the joint defect. The former uses a weighting method to compensate the errors of fitting; while the latter uses radial basis functions to find the relationship of input and output in order to modeling the surface. Both of the methods are applied to reconstruct various curved surfaces. The experimental results show that radial basis function neural network is better to restore the damaged knee joint surface. Also, at the easily damaged condyle part of the knee joint, the maximum approximation error is 0.171㎜ and the average approximation error is only 0.052㎜ even if the defect part of the reconstruction area reaches 49.7%. Based on the results, the radial basis function neural network is recommended to be used to generate resurfacing implants for the reconstruction of the knee joint defect.

關鍵字(中)

★ 徑向基底函數神經網路
★ 移動式最小平方法
★ 膝關節表面重建

關鍵字(英)

★ radial basis function neural network
★ moving least squares
★ knee joint
★ joint resurfacing reconstruction

論文目次

摘要 I
Abstract II
目錄 III
圖目錄 V
表目錄 VIII
第1章緒論 1
1-1 研究動機 1
1-2 文獻回顧 3
1-3 研究方法簡介 5
1-4 論文介紹 6
第2章膝關節局部表面重建方法 7
2-1 最小平方法 7
2-1-1 以最小平方法建立曲面 7
2-1-2 最小平方法的缺點 9
2-2 移動式最小平方法 10
2-2-1 何謂移動式最小平方法 10
2-2-2 應用移動式最小平方法描述及修補曲面 12
2-3 類神經網路簡介 14
2-3-1 什麼是類神經網路 14
2-3-2 為什麼使用類神經網路 14
2-3-3 類神經網路的基本模型 15
2-3-4 類神經網路的特性 16
2-4 徑向基底函數神經網路 17
2-4-1 徑向基底函數神經網路的基本架構 17
2-4-2 徑向基底函數中心的選取 19
2-4-3 徑向基底函數神經網路的學習演算法 22
2-4-4 應用徑向基底函數神經網路描述及修補曲面 24
第3章膝關節局部表面重建操作步驟 27
3-1 軟體建構基礎 27
3-2 操作步驟 28
第4章實驗結果與討論 36
4-1 不同曲面對修補結果的影響 36
4-1-1 對不同曲面修補的誤差 36
4-1-2 對不同曲面修補的效率 40
4-2 破損區域對修補結果的影響 41
4-2-1 破損區域對修補誤差的影響 41
4-2-2 破損區域對修補效率的影響 45
4-3 修補膝關節模型 48
4-3-1 內髁部份的大型缺損 49
4-3-2 內髁附近的小型缺損 52
4-3-3 內髁後側附近的大型缺損 55
4-3-4 內髁後側附近的小型缺損 58
4-3-5 其他部位的缺損 61
4-4 使用RBFNN修補膝關節模型上之特定區域 66
4-4-1 股骨與髕骨相接處 66
4-4-2 內髁附近 69
第5章結論 73
參考文獻 76

參考文獻

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指導教授

曾清秀(Ching-Shiow Teseng)

審核日期

2008-7-23

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