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    請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/2804


    題名: C-arm影像與電腦斷層影像之方位校準方法;2D-3D Registration
    作者: 戴君益;Chun-Yi Tai
    貢獻者: 機械工程研究所
    關鍵詞: 最小侵入式手術;脊椎手術;手術導引;2D-3D方位校準;Navigation System;2D-3D Registration;Spine surgery
    日期: 2006-01-12
    上傳時間: 2009-09-21 11:56:31 (UTC+8)
    出版者: 國立中央大學圖書館
    摘要: 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°。 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.
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