隨著高齡化社會及網路新世代的來臨，脊椎退化性疾病的人數越來越多。脊椎融合手術(Spinal fusion surgery)是常用的手術治療方式，其方式為將椎間盤病灶移除，並於其上下節椎骨間置入椎籠(Cage)，並以椎莖螺釘(Pedicle screw)固定椎骨，以防止椎骨的旋轉及移位以達到融合的目的(骨質疏鬆,脊椎損傷也會植入椎莖螺絲)。醫師在植入椎莖螺釘前必須高度仰賴C-arm X光影像才能確認鑽孔器械方位是否在椎莖安全範圍內，造成手術時間拉長，病患與醫療人員也因此吸收高放射線劑量，且因是人手操刀，所以非常仰賴醫師的手感及臨床經驗。本研究使用實驗室所研發的C-arm影像輔助導航系統結合協作式機械臂來協助醫師進行椎莖螺釘植入所需的鑽孔方位導引，由醫師於電腦螢幕顯示的兩張不同角度拍攝之脊椎C-arm影像分別點選椎莖鑽孔的進入點及結束點，透過雙角度攝影空間定位技術計算出鑽孔路徑的空間位置與方向，並將其轉至機械臂座標系上，由機械臂自動將鑽孔導槽定位至規劃的鑽孔路徑方位上，並隨著呼吸即時更新鑽孔路徑，使鑽孔導槽與鑽孔路徑達到同步移動的效果，醫師只需將鑽孔器械依循該導槽進行椎莖鑽孔即可。
本實驗使用SIEMENS公司Siremobil Compact L機型的C-arm設備，以一光學式定位器械置入醫師於假骨椎莖所鑽的孔洞內，並紀錄其方位作為對照組(A)，以C-arm影像輔助導航系統的Bi-plane方法計算的路徑方位(A^')，與機械手臂將導槽對準規畫的鑽孔路徑後置入光學式定位器械(A^'')的方位作為實驗組。經過數次的實驗，A與A^'的器械尖點距離平均誤差為2.2mm、標準差為0.7mm，軸向平均誤差為2.7°、標準差為2.1°；A^'與A^''的器械尖點距離平均誤差為1.2mm、標準差為0.3mm，軸向平均誤差為0.4°、標準差為0.1°；A與A^''的器械尖點距離平均誤差為2.9mm、標準差為0.5mm，軸向平均誤差為2.8°、標準差為2.3°，而套入機械手臂同步追蹤功能的系統總距離平均誤差為2.8mm、標準差為0.4mm，軸向平均誤差為0.5°、標準差為0.03°。
關鍵字： C-arm影像、手術導引、協作型機械手臂、呼吸追蹤、脊椎手術

Due to highly rely on the C-arm X ray imaging during operation, the surgeon would need a lengthy time to implant the pedicle screw into right position and orientation in pedicle, and thus raise a risk for medical personnel and the patient to expose in a high-radiation-dose environment. Furthermore, the operation is highly relying on the doctor’s artifice and clinical experiences. So this research is to develop a C-arm image assisted robotic navigation system to assist surgeons to implant pedicle screws to the correct location. The surgeon can click the entry point and end point of drill bit on the computer-displayed C-arm X-ray images, and the bi-plane method will be applied to calculate the spatial position and orientation of the drill path, which are input to the robot controller so that the robot is able to automatically move the guide sleeve to align with the planned path. Then the surgeon can drill the pedicle by drilling along the guide sleeve.
This research uses a Siremobil Compact L C-arm equipment produced by SIEMENS Co. A probe tracked by optical tracker is placed at the drilling inlet of the pedicle, and the tip position and orientation of the probe are recorded as the control group (A). The path that calculated by the Bi-plane method was recorded as the first experimental group (A^'), and the path determined by the probe placed into the guide sleeve was recorded as the other experimental group (A^''). By repeating the tests for several times, the experimental results indicate that the average distance error of tool tip between groups A and A^' is 2.2mm±0.5mm and the average direction error between the paths 2.7 ±2.1 . Similarly, the average distance error of tool tip between A^' and A^'' is 1.2mm±0.3mm, the average direction error is 0.4 ±0.1 , the average distance error of tool tip between A and A^'' is 2.9mm±0.5mm, and the average direction error is 2.8 ±2.3 . Moreover, the positioning experiment of the robotic navigation system with motion tracking function between A and A^'' is 2.8mm±0.4mm and the average direction error is 0.5°±0.03 . The overall position errors are larger than the expected 2mm required for clinic applications and the direction errors are larger than the expected 2 degrees, either.

Keyword: C-arm Image, Surgical Navigation, Collaborative Robot, Respiratory Tracking, Spine Surgery.

1. 吳吉春, 基於 C-arm 影像的手術導引定位. 中央大學機械工程學系學位論文, 2012: p. 1-60.
2. Foley, K.T., D.A. Simon, and Y.R. Rampersaud, Virtual fluoroscopy: computer-assisted fluoroscopic navigation. Spine, 2001. 26(4): p. 347-351.
3. http://www.medtronic.com/, Medtronic Inc.
4. https://www.brainlab.com/, Brainlab Inc.
5. Yaniv, Z. and L. Joskowicz, Precise robot-assisted guide positioning for distal locking of intramedullary nails. IEEE transactions on medical imaging, 2005. 24(5): p. 624-635.
6. Hu, X. and I.H. Lieberman, Robotic-Assisted Spine Surgery, in Minimally Invasive Spine Surgery. 2014, Springer. p. 61-66.
7. Lonjon, N., et al., Robot-assisted spine surgery: feasibility study through a prospective case-matched analysis. European Spine Journal, 2016. 25(3): p. 947-955.
8. Elfring, R., M. de la Fuente, and K. Radermacher, Assessment of optical localizer accuracy for computer aided surgery systems. Computer Aided Surgery, 2010. 15(1-3): p. 1-12.
9. Wang, S.-W., 使用機械手臂輔助椎莖螺釘植入之手術導引系統. 2016, National Central University.
10. 徐永倫, 具呼吸補償功能之超音波影像輔助機械手臂 HIFU 腫瘤燒灼追蹤系統. 中央大學生物醫學工程研究所學位論文, 2015: p. 1-80.
11. Kalman, R.E., A new approach to linear filtering and prediction problems. Journal of basic Engineering, 1960. 82(1): p. 35-45.
12. Allotta, B., et al., A new AUV navigation system exploiting unscented Kalman filter. Ocean Engineering, 2016. 113: p. 121-132.
13. Zhao, Y., Performance evaluation of cubature Kalman filter in a GPS/IMU tightly-coupled navigation system. Signal Processing, 2016. 119: p. 67-79.
14. Moore, T. and D. Stouch, A generalized extended kalman filter implementation for the robot operating system, in Intelligent Autonomous Systems 13. 2016, Springer. p. 335-348.
15. Boaventura, T., L. Hammer, and J. Buchli, Interaction Force Estimation for Transparency Control on Wearable Robots Using a Kalman Filter, in Converging Clinical and Engineering Research on Neurorehabilitation II. 2017, Springer. p. 489-493.
16. 陳冠君, 整合 EPnP 及導引器械之 C-arm 影像輔助脊椎手術用導引系統. 中央大學機械工程學系學位論文, 2015: p. 1-82.
17. 謝仁懋, C-arm 影像導引系統於臨床椎弓螺釘植入之應用與改良. 中央大學生物醫學工程研究所學位論文, 2012: p. 1-70.
18. Fahrig, R., M. Moreau, and D. Holdsworth, Three‐dimensional computed tomographic reconstruction using a C‐arm mounted XRII: Correction of image intensifier distortion. Medical physics, 1997. 24(7): p. 1097-1106.
19. https://www.iso.org/standard/62996.html, ISO.
20. Halliday, A., et al., Dorsal thoracic and lumbar screw fixation and pedicle fixation techniques. Spine Surgery: Techniques, Complication Avoidance, and Management. New York: Churchill-Livingstone, 1999: p. 1053-1064.
21. Eddy, S.R., Hidden markov models. Current opinion in structural biology, 1996. 6(3): p. 361-365.