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Influence of multi-angle input of intraoperative fluoroscopic images on the spatial positioning accuracy of the C-arm calibration-based algorithm of a CAOS system

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Abstract

Intraoperative fluoroscopic images, as one of the most important input data for computer-assisted orthopedic surgery (CAOS) systems, have a significant influence on the positioning accuracy of CAOS system. In this study, we proposed to use multi-angle intraoperative fluoroscopy images as input based on real clinical scenario, and the aim was to analyze the positioning accuracy and the error propagation rules with multi-angle input images compared with traditional two input images. In the experiment, the positioning accuracy of the C-arm calibration-based algorithm was studied, respectively, using two, three, four, five, and six intraoperative fluoroscopic images as input data. Moreover, the error propagation rules of the positioning error were analyzed by the Monte Carlo method. The experiment result showed that increasing the number of multi-angle input fluoroscopic images could reduce the positioning error of CAOS system, which has dropped from 1.01 to 0.61 mm. The Monte Carlo simulation analysis showed that for random input errors subject to normal distribution (μ = 0, σ = 1), the image positioning error dropped from 0.29 to 0.23 mm, and the staff gauge positioning error dropped from 1.36 to 1.19 mm, while the tracking device positioning error dropped from 3.41 to 2.13 mm. In addition, the results showed that image positioning error and staff gauge positioning error were all nonlinear error for the whole system, but tracker device positioning error was a strictly linear error. In conclusion, using multi-angle fluoroscopy images was helpful for clinic, which could improve the positioning accuracy of the CAOS system by nearly 30%.

The experiment process and Monte Carlo analysis of spatial positioning accuracy (A: Setup for the experiment; B: The process of Monte Carlo analysis; C: Results)

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References

  1. Hernandez D, Garimella R, Eltorai AE, Daniels AH (2017) Computer-assisted Orthopaedic surgery. Orthop Sugr 9(2):152–158. https://doi.org/10.1111/os.12323

    Article  Google Scholar 

  2. Kanlić EM, DeLaRosa F, Pirela-Cruz M (2006) Computer assisted orthopaedic surgery–CAOS. Bosnian J Basic Med 6(1):7–14. https://doi.org/10.17305/bjbms.2006.3202

    Article  Google Scholar 

  3. Zheng G, Nolte LP (2015) Computer-assisted orthopedic surgery: current state and future perspective. Front Surg 2:66. https://doi.org/10.3389/fsurg.2015.00066

    Article  PubMed  PubMed Central  Google Scholar 

  4. Goradia VK (2014) Computer-assisted and robotic surgery in orthopedics: where we are in 2014. Sports Med Arthrosc 22(4):202–205. https://doi.org/10.1097/JSA.0000000000000047

    Article  PubMed  Google Scholar 

  5. Bignozzi S, Lopomo N, Zaffagnini S, Martelli S, Bruni D, Marcacci M (2008) Accuracy, reliability, and repeatability of navigation systems in clinical practice. Oper Tech Orthop 18(3):154–157. https://doi.org/10.1053/j.oto.2008.11.001

    Article  Google Scholar 

  6. Schep NW, Broeders IAMJ, van der Werken C (2003) Computer assisted orthopaedic and trauma surgery: state of the art and future perspectives. Injury 34(4):299–306. https://doi.org/10.1016/S0020-1383(01)00208-X

    Article  CAS  PubMed  Google Scholar 

  7. Nolte LP, Beutler T (2004) Basic principles of CAOS. Injury 35(1):6–16. https://doi.org/10.1016/j.injury.2004.05.005

    Article  Google Scholar 

  8. Luebbers HT, Messmer P, Obwegeser JA, Zwahlen RA, Kikinis R, Graetz KW, Matthews F (2008) Comparison of different registration methods for surgical navigation in cranio-maxillofacial surgery. J Cranio Maxill Surg 36(2):109–116. https://doi.org/10.1016/j.jcms.2007.09.002

    Article  Google Scholar 

  9. Zheng G, Kowal J, Ballester MAG, Caversaccio M, Nolte LP (2007) (i) Registration techniques for computer navigation. Curr Orthopaed 21(3):170–179. https://doi.org/10.1016/j.cuor.2007.03.002

    Article  Google Scholar 

  10. Phillips R (2007) (ii) the accuracy of surgical navigation for orthopaedic surgery. Curr Orthop 21(3):180–192. https://doi.org/10.1016/j.cuor.2007.06.006

    Article  Google Scholar 

  11. Livyatan H, Yaniv Z, Joskowicz L (2002) Robust automatic C-arm calibration for fluoroscopy-based navigation: a practical approach. International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 60–68. https://doi.org/10.1007/3-540-45787-9_8

    Book  Google Scholar 

  12. Kraus M, von dem Berge S, Schöll H, Krischak G, Gebhard F (2013) Integration of fluoroscopy-based guidance in orthopaedic trauma surgery–a prospective cohort study. Injury 44(11):1486–1492. https://doi.org/10.1016/j.injury.2013.02.008

    Article  PubMed  Google Scholar 

  13. Suhm N, Jacob AL, Nolte LP, Regazzoni P, Messmer P (2000) Surgical navigation based on fluoroscopy-clinical application for computer-assisted distal locking of intramedullary implants. Comput Aided Surg 5(6):391–400. https://doi.org/10.3109/10929080009148899

    Article  CAS  PubMed  Google Scholar 

  14. Meng C, Zhang J, Zhou F, Wang T (2014) New method for geometric calibration and distortion correction of conventional C-arm. Comput Biol Med 52:49–56. https://doi.org/10.1016/j.compbiomed.2014.06.009

    Article  PubMed  Google Scholar 

  15. Faugeras O (1993) Three-dimensional computer vision: a geometric viewpoint. MIT Press, Massachusetts State

    Google Scholar 

  16. Schueler BA (1995) Hu X (1995) correction of image intensifier distortion for three-dimensional x-ray angiography. Medical Imaging 2432:272–279. https://doi.org/10.1117/12.208345

    Article  Google Scholar 

  17. Liu RR, Rudin S, Bednarek DR (1999) Super-global distortion correction for a rotational C-arm x-ray image intensifier. Med Phys 26(9):1802–1810. https://doi.org/10.1118/1.598684

    Article  CAS  PubMed  Google Scholar 

  18. Mennessier C, Spencer B, Clackdoyle R, Conneau AC, Xu T (2011) Distortion correction, geometric calibration, and volume reconstruction for an isocentric C-arm X-ray system. 2011 IEEE Nuclear Sci Symp Conf Record 2011:2943–2947. https://doi.org/10.1109/NSSMIC.2011.6152525

    Article  Google Scholar 

  19. Gronenschild E (1999) Correction for geometric image distortion in the x-ray imaging chain: local technique versus global technique. Med Phys 26(12):2602–2616. https://doi.org/10.1118/1.598800

    Article  CAS  PubMed  Google Scholar 

  20. Cerveri P, Forlani C, Borghese NA, Ferrigno G (2002) Distortion correction for x-ray image intensifiers: local unwarping polynomials and RBF neural networks. Med Phys 29(8):1759–1771. https://doi.org/10.1118/1.1488602

    Article  CAS  PubMed  Google Scholar 

  21. Liu L, Bassano DA, Prasad SC, Keshler BL, Hahn SS (2003) On the use of C-arm fluoroscopy for treatment planning in high dose rate brachytherapy. Med Phys 30(9):2297–2302. https://doi.org/10.1118/1.1598851

    Article  PubMed  Google Scholar 

  22. Fantozzi S, Cappello A, Leardini A (2003) A global method based on thin-plate splines for correction of geometric distortion: an application to fluoroscopic images. Med Phys 30(2):124–131. https://doi.org/10.1118/1.1538228

    Article  PubMed  Google Scholar 

  23. Zhou X , Meng C , Fan L , Lv S (2009) The research of global correction for C-arm X-ray image based on pin-hole model. Proceedings of the 2nd international conference on BioMedical engineering and informatics 1-5. https://doi.org/10.1109/BMEI.2009.5305703

  24. Soimu D, Badea C, Pallikarakis N (2003) A novel approach for distortion correction for X-ray image intensifiers. Comput Med Imag Grap 27(1):79–85. https://doi.org/10.1016/S0895-6111(02)00055-1

    Article  Google Scholar 

  25. Yan S, Nie S, Zheng B (2011) Improving accuracy of XRII image distortion correction using a new hybrid image processing method: performance assessment. Med Phys 38(11):5921–5932. https://doi.org/10.1118/1.3644846

    Article  PubMed  Google Scholar 

  26. Canero C, Nofrerías E, Mauri J, Radeva P (2002) Modelling the acquisition geometry of a C-arm angiography system for 3D reconstruction. Catalonian Conf Artif Intell 2002:322–335. https://doi.org/10.1007/3-540-36079-4_28

    Article  Google Scholar 

  27. Gorges S, Kerrien E, Berger MO, Trousset Y, Picard L (2005) Model of a vascular C-arm for 3D augmented fluoroscopy in interventional radiology. Int Conf Med Image Comput Comput Assist Interv 2005:214–222. https://doi.org/10.1007/11566489_27

    Article  Google Scholar 

  28. Panetta D, Belcari N, Del Guerra A, Moehrs S (2008) An optimization-based method for geometrical calibration in cone-beam CT without dedicated phantoms. Phys Med Biol 53(14):3841. https://doi.org/10.1088/0031-9155/53/14/009

    Article  CAS  PubMed  Google Scholar 

  29. Meng Y, Gong H, Yang X (2012) Online geometric calibration of cone-beam computed tomography for arbitrary imaging objects. IEEE T Med Imaging 32(2):278–288. https://doi.org/10.1109/TMI.2012.2224360

    Article  Google Scholar 

  30. Letournel E, Judet R (2012) Fractures of the acetabulum. Springer Science & Business Media, Berlin

    Google Scholar 

  31. Tian W (2016) Robot-assisted posterior C1–2 transarticular screw fixation for atlantoaxial instability: a case report. Spine 41:B2–B5. https://doi.org/10.1097/BRS.0000000000001674

    Article  PubMed  Google Scholar 

  32. Wang J, Wang Y, Zhu G, Chen X, Zhao X, Qiao H, Fan Y (2018) Influence of the quality of intraoperative fluoroscopic images on the spatial positioning accuracy of a CAOS system. Int J Med Robot Comp 14(3):e1898. https://doi.org/10.1002/rcs.1898

    Article  Google Scholar 

  33. Pei B, Zhu G, Wang Y, Qiao H, Chen X, Wang B, Li X, Zhang W, Liu W, Fan Y (2017) The development and error analysis of a kinematic parameters based spatial positioning method for an orthopedic navigation robot system. Int J Med Robot Comp 13(3):e1782. https://doi.org/10.1002/rcs.1782

    Article  Google Scholar 

  34. Wübbeler G, Krystek M, Elster C (2008) Evaluation of measurement uncertainty and its numerical calculation by a Monte Carlo method. Meas Sci Technal 19(8):084009. https://doi.org/10.1088/0957-0233/19/8/084009

    Article  CAS  Google Scholar 

  35. Santolaria J, GinéS M (2013) Uncertainty estimation in robot kinematic calibration. Robot Cim-int Manuf 29(2):370–384. https://doi.org/10.1016/j.rcim.2012.09.007

    Article  Google Scholar 

  36. Gao H, Luo C, Hu C, Zhang C, Zeng B (2010) Percutaneous screw fixation of acetabular fractures with 2D fluoroscopy-based computerized navigation. Arch Orthop Traum Su 130(9):1177–1183. https://doi.org/10.1007/s00402-010-1095-2

    Article  Google Scholar 

  37. Merloz P, Trocca J, Vouaillat H, Vasile C, Tonetti J, Eid A, Plaweski S (2007) Fluoroscopy-based navigation system in spine surgery. Proc Inst Mech Eng H J Eng Med 221(7):813–820. https://doi.org/10.1243/09544119JEIM268

    Article  CAS  Google Scholar 

Download references

Funding

This study was funded by National Key Research and Development Program of China (2017YFC0110602 and 2016YFC1100704), National Natural Science Foundation (NSFC) Grant of China (61871019), Beijing science and technology project (Z161100000116023).

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Authors and Affiliations

  1. School of Biological Science and Medical Engineering, Beihang University, 37# Xueyuan Road, Haidian District, Beijing, 100191, China

    Xiangqian Chen, Yu Wang, Gang Zhu, Gang Zhou & Yubo Fan

  2. Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, 37# Xueyuan Road, Haidian District, Beijing, 100191, China

    Yu Wang, Gang Zhou & Yubo Fan

  3. Beijing TINAVI Medical Technology Co., Ltd, 66# Xixiaokou Road, Haidian District, Beijing, 100192, China

    Weijun Zhang

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  1. Xiangqian Chen
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Correspondence to Yu Wang.

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Chen, X., Wang, Y., Zhu, G. et al. Influence of multi-angle input of intraoperative fluoroscopic images on the spatial positioning accuracy of the C-arm calibration-based algorithm of a CAOS system. Med Biol Eng Comput 58, 559–572 (2020). https://doi.org/10.1007/s11517-019-02112-9

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