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Intraoperative detection and localization of cylindrical implants in cone-beam CT image data

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Orthopedic fractures are often fixed using metal implants. The correct positioning of cylindrical implants such as surgical screws, rods and guide wires is highly important. Intraoperative 3D imaging is often used to ensure proper implant placement. However, 3D image interaction is time-consuming and requires experience. We developed an automatic method that simplifies and accelerates location assessment of cylindrical implants in 3D images.

Methods

Our approach is composed of three major steps. At first, cylindrical characteristics are detected by analyzing image gradients in small image regions. Next, these characteristics are grouped in a cluster analysis. The clusters represent cylindrical implants and are used to initialize a cylinder-to-image registration. Finally, the two end points are optimized regarding image contrast along the cylinder axis.

Results

A total of 67 images containing 420 cylindrical implants were used for testing. Different anatomical regions (calcaneus, spine) and various image sources (two mobile devices, three reconstruction methods) were investigated. Depending on the evaluation set, the detection performance was between 91.7 and 96.1 % true- positive rate with a false-positive rate between 2.0 and 3.2 %. The end point distance errors ranged from \(1.0 \pm 1.2\) to \(4.3 \pm 2.9\) mm and the orientation errors from \(1.6 \pm 2.2\) to \(2.3 \pm 2.2\) degrees. The average computation time was less than 5 seconds.

Conclusions

An automatic method was developed and tested that obviates the need for 3D image interaction during intraoperative assessment of cylindrical orthopedic implants. The required time for working with the viewing software of cone-beam CT device is drastically reduced and leads to a shorter time under anesthesia for the patient.

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References

  1. Barth K, Dennerlein F, Brunner T, Fieselmann A, Graumann R (2013) Infinite impulse response filtering for cone beam tomography. In: Proceedings of SPIE 8668, medical imaging 2013, physics of medical, imaging, 86682U

  2. Chaperon T, Goulette F (2001) Extracting cylinders in full 3D data using a random sampling method and the Gaussian image. In: Vision modeling and visualization, pp 35–42

  3. Duda RO, Hart PE (1972) Use of the hough transformation to detect lines and curves in pictures. Commun ACM 15(1):11–15

    Article  Google Scholar 

  4. Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Knowledge discovery and data mining, pp 226–231

  5. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395

    Article  Google Scholar 

  6. Franke J, von Recum J, Suda AJ, Grützner PA, Wendl K (2012) Intraoperative three-dimensional imaging in the treatment of acute unstable syndesmotic injuries. J Bone Joint Surg 94(15):1386–1390

    Article  PubMed  Google Scholar 

  7. Franke J, Recum J, Wendl K, Grützner P (2013) Intraoperative three-dimensional imaging—beneficial or necessary? Der Unfallchirurg 116(2):185–190

    Article  PubMed  CAS  Google Scholar 

  8. Geerling J, Kendoff D, Citak M, Zech S, Gardner MJ, Hüfner T, Krettek C, Richter M (2009) Intraoperative 3d imaging in calcaneal fracture care-clinical implications and decision making. J Trauma Acute Care Surg 66(3):768–773

    Article  Google Scholar 

  9. Kendoff D, Citak M, Gardner MJ, Stübig T, Krettek C, Hüfner T (2009) Intraoperative 3d imaging: value and consequences in 248 cases. J Trauma Acute Care Surg 66(1):232–238

    Article  Google Scholar 

  10. Meyer E, Raupach R, Lell M, Schmidt B, Kachelrieß M (2012) Frequency split metal artifact reduction (fsmar) in computed tomography. Med Phys 39(4):1904–1916

    Google Scholar 

  11. Nolden M, Zelzer S, Seitel A, Wald D, Müller M, Franz AM, Maleike D, Fangerau M, Baumhauer M, Maier-Hein L, Maier-Hein KH, Meinzer HP, Wolf I (2013) The medical imaging interaction toolkit: challenges and advances. Int J Comput Assist Radiol Surg 8(4):607–620

    Article  PubMed  Google Scholar 

  12. Otsu N (1975) A threshold selection method from gray-level histograms. Automatica 11:285–296

    Article  Google Scholar 

  13. Pauwels R, Jacobs R, Bosmans H, Pittayapat P, Kosalagood P, Silkosessak O, Panmekiate S (2013) Automated implant segmentation in cone-beam ct using edge detection and particle counting. Int J Comput Assist Radiol Surg (Epub ahead of print)

  14. Qiu W, Yuchi M, Ding M, Tessier D, Fenster A (2013) Needle segmentation using 3d hough transform in 3d trus guided prostate transperineal therapy. Med Phys 40(4):042902

    Article  PubMed  Google Scholar 

  15. Rabbani T, van den Heuvel F (2005) Efficient hough transform for automatic detection of cylinders in point clouds. In: ISPRS WG III/3, III/4, V/3 Workshop laser, scanning, pp 60–65

  16. Richter M, Zech S (2009) Intraoperative 3-dimensional imaging in foot and ankle trauma-experience with a second-generation device (arcadis-3d). J Orthop Trauma 23(3):213–220

    Article  PubMed  Google Scholar 

  17. Roberts KS (1988) A new representation for a line. In: Computer vision and pattern recognition, pp 635–640

  18. Rübberdt A, Feil R, Stengel D, Spranger N, Mutze S, Wich M, Ekkernkamp A (2006) The clinical use of the iso-c (3d) imaging system in calcaneus fracture surgery]. Der Unfallchirurg 109(2):112–118

    Article  PubMed  Google Scholar 

  19. Sanders R (2000) Current concepts review—displaced intra-articular fractures of the calcaneus. J Bone Joint Surg 82(2):225–250

    PubMed  CAS  Google Scholar 

  20. Schnabel R, Wahl R, Klein R (2007) Efficient ransac for point-cloud shape detection. Comput Graph Forum 26:214–226

    Article  Google Scholar 

  21. Styner M, Brechbühler C, Székely G, Gerig G (2000) Parametric estimate of intensity inhomogeneities applied to MRI. IEEE Trans Med Imaging 19:153–165

    Article  PubMed  CAS  Google Scholar 

  22. Vosselman G, Gorte BG, Sithole G, Rabbani T (2004) Recognising structure in laser scanner point clouds. Int Arch Photogramm Remote Sens Sp Inf Sci 46:33–38

    Google Scholar 

  23. Zhou H, Qiu W, Ding M, Zhang S (2008) Automatic needle segmentation in 3d ultrasound images using 3d improved hough transform. In: Proceedings of SPIE 6918, medical imaging 2008, image-guided procedures and modeling, 691821

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Acknowledgments

This work was partially funded by Siemens Healthcare, X-ray Products.

Conflict of interest

Authors declare no conflict of interest.

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Authors and Affiliations

  1. Division of Medical and Biological Informatics (E130), German Cancer Research Center, Im Neuenheimer Feld 280, 69120 , Heidelberg, Germany

    Joseph Görres, Michael Brehler, Hans-Peter Meinzer & Diana Nabers

  2. BG Trauma Center, Ludwig-Guttmann-Straße 13, 67071 , Ludwigshafen am Rhein, Germany

    Jochen Franke, Sven Y. Vetter & Paul A. Grützner

  3. Siemens AG, Healthcare Sector, Henkestr. 127, 91052 , Erlangen, Germany

    Karl Barth

  4. Mannheim University of Applied Sciences, Paul-Wittsack-Str. 10, 68163 , Mannheim, Germany

    Ivo Wolf

  5. Clínica Alemana de Santiago, Avenida Vitacura 5951, Santiago de Chile, Chile

    Andrés Córdova

Authors
  1. Joseph Görres
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  2. Michael Brehler
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  3. Jochen Franke
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  4. Karl Barth
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  5. Sven Y. Vetter
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  6. Andrés Córdova
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  7. Paul A. Grützner
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  8. Hans-Peter Meinzer
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  9. Ivo Wolf
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  10. Diana Nabers
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Corresponding author

Correspondence to Joseph Görres.

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Görres, J., Brehler, M., Franke, J. et al. Intraoperative detection and localization of cylindrical implants in cone-beam CT image data. Int J CARS 9, 1045–1057 (2014). https://doi.org/10.1007/s11548-014-0998-8

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  • DOI: https://doi.org/10.1007/s11548-014-0998-8

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