應用輪廓與灰階特徵於二維C-arm影像及三維電腦斷層影像之方位校準

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姓名

朱鴻宇(Hung-Yu Chu) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

應用輪廓與灰階特徵於二維C-arm影像及三維電腦斷層影像之方位校準
(Registration of 2D C-arm images and 3D CT images using contour and intensity information.)

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摘要(中)

現今的手術導引系統多半皆使用單一的影像如電腦斷層影像(CT)、核磁共振影像(MRI)或C-arm X光影像，對於微創手術（Minimal Invasive Surgery）言，若能以術前的電腦斷層影像或核磁共振影像作三維的手術規劃，再以術中的二維C-am影像進行手術定位，則微創手術將更精準與可靠。本研究發展一套能夠整合C-arm影像及三維電腦斷層影像的方位校準方法，此方法不同於一般使用CT影像的手術導引系統，只需於術中拍攝兩張C-arm影像，即可完成病患與CT影像間的方位校準，以應用在微創手術。
首先將C-arm影像扭正並計算其發射源，然後選取C-arm影像與CT影像的相同特徵點，利用特徵點的座標完成病患座標系與CT模型座標系的初始方位校準，之後以濺射成像法(Splat Rendering)產生模擬C-arm影像的數位重建X-ray影像（Digital Reconstructed Radiograph, DRR），再以最佳化方法搜尋最佳的方位校準矩陣，使DRR影像與C-arm影像達到最大相似度。本研究以灰階基礎的影像相似度量測(Intensity Based Similarity Measure)結合特徵基礎影像相似度量測（Feature Based Similarity Measure）中的輪廓方法進行影像比對量測，並使用基因演算法（Genetic Algorithm）疊代運算完成方位校準的最佳化。實驗使用脊椎塑膠切骨模型（White Plastic Saw Bone），結果顯示DRR影像輪廓的平均定位誤差為0.26mm，平均角度誤差為0.78°；C-arm影像輪廓的平均定位誤差為0.53mm，平均角度誤差為1.45°；而在針對椎體(Vertebral Body)的特徵區域圈選實驗上，平均位移誤差及平均角度誤差分別為1.09mm與3.01度。

摘要(英)

Most image-guided surgical navigation systems use single image modality such as computed tomography, magnetic resonance imaging, or C-arm X-ray imaging. It will enhance the precision and reliability of minimally invasive surgery if there are preoperative CT or MRI images for surgical planning and intraoperative C-arm images for surgical positioning. This research develops a 2D-3D registration method for the mapping between preoperative CT images and intraoperative C-arm images. The approach is different to that of CT image based navigation system. It only needs two intraoperative C-arm images to complete the registration and can be applied to minimally invasive surgery.
First, the C-arm images are calibrated and the focus point of X-ray is determined. Then, select three identical characteristic points in C-arm images and CT images to carry out the initial registration between the patient and its CT reconstructed model. After that, splat rendering is used to generate digital reconstructed radiographs (DRRs) to simulate C-arm images. Through the similarity measure of C-arm images and DRRs, the optimum transformation matrix can be found by applying optimization algorithms. This research combines the intensity based similarity measure method and the contour information of the feature based similarity measure method. Then, genetic algorithm is adopted to optimize the similarity rate and optimize the registration.
In the experiment, a white plastic saw bone model of spine is used. The average registration accuracy of location and orientation of DRR images are 0.26mm and 0.77° respectively. And the registration accuracy of location and orientation of C-arm contour information are 0.53mm and 1.45° respectively. Finally, the accuracy of location and orientation of a region of interest selection experiment stressed on vertebral body are 1.09mm and 3.01° respectively.

關鍵字(中)

★ 2D-3D方位校準
★ 影像相似性量測
★ 最小侵入式手術

關鍵字(英)

★ 2D-3D Registration
★ Minimal invasive surgery
★ Image similarity Measure

論文目次

摘要 I
ABSTRACT II
誌謝 IV
目錄 V
圖目錄 VIII
表目錄 XIII
第1章緒論 1
1-1 研究動機 1
1-2 文獻回顧 2
1-2-1 DRR影像的產生 3
1-2-2 DRR影像與C-arm影像的比對方式 4
1-2-3 方位轉換矩陣的最佳化 5
1-2-4 其他相關細節 5
1-3 研究方法簡介 6
1-4 論文介紹 7
第2章系統架構 8
2-1 系統座標系統 8
2-1-1 座標系轉換關係 10
2-1-2 病患與CT影像座標系之定義 11
2-2 硬體架構 14
2-2-1 光學式定位裝置 14
2-2-2 C-arm影像校正器 16
2-2-3 手術器械 17
2-3 軟體架構 17
2-3-1 主要介面介紹 18
2-3-2 特徵區域圈選工具 19
第3章研究方法 21
3-1 C-ARM影像扭正及發射源計算 23
3-2 初始方位校準 24
3-3 濺射法建構DRR影像 26
3-4 影像相似性量測 29
3-4-1 數種影像灰階基礎之比對方式的介紹 29
3-4-2 灰階基礎影像相似性量測可靠性分析一 31
3-4-3 灰階基礎影像相似性量測可靠性分析二 37
3-4-4 結合輪廓量測方法於灰階基礎影像相似性量測中 40
3-5 最佳化方法 46
第4章實驗及討論 50
4-1 理想方位校準矩陣之獲得 51
4-2 誤差分析方法 53
4-3 方位校準實驗 54
4-3-1 理想解DRR輪廓實驗 55
4-3-2 真實C-arm輪廓實驗 57
4-3-3 ROI區域特徵實驗 62
4-3-4 方位校準實驗總結 64
4-4 系統誤差分析 64
4-4-1 光學式定位誤差 64
4-4-2 C-arm影像校正以及相關參數誤差 64
4-4-3 人為操作誤差 65
第5章結論 66
參考文獻 68

參考文獻

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指導教授

曾清秀(Ching-Shiow Teseng)

審核日期

2008-1-24

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