Skip to main content

Advertisement

Log in

Computer-assisted fracture reduction: a new approach for repositioning femoral fractures and planning reduction paths

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Reduction is a crucial step in the surgical treatment of bone fractures to achieve anatomical alignment and facilitate healing. Surgical planning for treatment of simple femoral fractures requires suitable gentle reduction paths. A plan with optimal movement of fracture fragments from the initial to the desired target position should improve the reduction procedure. A virtual environment which repositions the fracture fragments automatically and provides the ability to plan reduction paths was developed and tested.

Methods

Virtual 3D osseous fragments are created from CT scans. Based on the computed surface curvatures, strongly curved edges are selected and fracture lines are generated. After assignment of matching points, the lines are compared and the desired target position is calculated. Planning of reduction paths was achieved using a reference-coordinate-system for the computation of reduction parameters. The fracture is reduced by changing the reduction parameters step by step until the target position is reached. To test this system, nine different fractured SYNBONE models and one human fracture were reduced, based on CT scans with varying resolution.

Results

The highest mean translational error is \(1.2 \pm 0.9\) (mm), and the rotational error is \(2.6 \pm 2.8\, (^{\circ })\), both of which are considered as clinically acceptable. The reduction paths can be planned manually or semi-automatically for each fracture.

Conclusions

Automated fracture reduction was achieved using a system based on preoperative CT scans. The automated system provides a clinically feasible basis for planning optimal reduction paths that may be augmented by further computer- or robot-assisted applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Rüedi T, Buckley RE, Morgan CG (2007) AO principles of fracture management, books and DVD, 2nd edn. Thieme, AO Pub, Stuttgart, New York

  2. Gardner MJ, Citak M, Citak M et al (2008) Navigated femoral anteversion measurements: a new intraoperative technique. Injury 39(4):467–471

    Article  PubMed  Google Scholar 

  3. G Zheng, X Dong, X Zhang et al (2005) Automated detection and segmentation of diaphyseal bone fragments from registered C-arm images for long bone fracture reduction. In: IEEE-EMBS 2005: 27th annual international conference of the Engineering in Medicine and Biology Society, Engineering in Medicine and Biology Society, 1–4 Sept 2005, Shanghai, China. IEEE, Piscataway, NJ, pp 4361–4364

  4. Hofstetter R, Slomczykowski M, Krettek C et al (2000) Computer-assisted fluoroscopy-based reduction of femoral fractures and antetorsion correction. Comput Aided Surg 5(5):311–325. doi:10.3109/10929080009149849

    Article  CAS  PubMed  Google Scholar 

  5. Hüfner T, Pohlemann T, Tarte S et al (2001) Computer-assisted fracture reduction: novel method for analysis of accuracy. Comput Aided Surg 6(3):153–159. doi:10.1002/igs.1018

    Article  PubMed  Google Scholar 

  6. Joskowicz L, Milgrom C, Simkin A et al (1998) Fracas: a system for computer-aided image-guided long bone fracture surgery. Comput. Aided Surg. 3(6):271–288. doi:10.3109/10929089809148148

    Article  CAS  PubMed  Google Scholar 

  7. Nakajima Y, Tashiro T, Sugano N et al (2007) Fluoroscopic bone fragment tracking for surgical navigation in femur fracture reduction by incorporating optical tracking of hip joint rotation center. IEEE Trans Biomed Eng 54(9):1703–1706. doi:10.1109/TBME.2007.900822

    Article  PubMed  Google Scholar 

  8. Oszwald M, Westphal R, Calafi A et al (2010) A standardized fracture reduction model for long bones-implication and evaluation in the femur. Technol Health Care 18(6):387–391

    PubMed  Google Scholar 

  9. Ron O, Joskowicz L, Milgrom C et al (2002) Computer-based periaxial rotation measurement for aligning fractured femur fragments from ct: a feasibility study. Comput Aided Surg 7(6):332–341. doi:10.1002/igs.10056

    Article  PubMed  Google Scholar 

  10. Schmucki D, Gebhard F, Grützner PA et al (2004) Computer aided reduction and imaging. Injury 35(1):96–104

    Article  Google Scholar 

  11. Tockus L, Joskowicz L, Simkin A et al (1998) Computer-aided image-guided bone fracture surgery: modeling, visualization, and preoperative planning. In: Medical image computing and computer-assisted interventation, MICCAI’98, vol 1496, pp 29–38. doi:10.1007/BFb0056185

  12. Gösling T, Westphal R, Faülstich J et al (2006) Forces and torques during fracture reduction: intraoperative measurements in the femur. J Orthop Res 24(3):333–338. doi:10.1002/jor.20045

    Article  PubMed  Google Scholar 

  13. Graham AE, Xie SQ, Aw KC et al (2008) Bone–muscle interaction of the fractured femur. J Orthop Res 26(8):1159–1165. doi:10.1002/jor.20611

    Article  PubMed  Google Scholar 

  14. Hung S, Lee M (2010) Functional assessment of a surgical robot for reduction of lower limb fractures. Int J Med Robot Comput Assist Surg 6(4):413–421

    Article  Google Scholar 

  15. Koo TKK, Mak AFT (2007) A bone reposition device for execution of ct-based diaphyseal fracture reduction. J Biomech 40:S283

    Article  Google Scholar 

  16. Matthews F, Neuhaus V, Schmucki D et al (2005) Passive pneumatic stabilization device for assisting in reduction of femoral shaft fractures. Eur J Trauma 31(6):568–574. doi:10.1007/s00068-005-2047-3

    Article  Google Scholar 

  17. Westphal R, Gösling T, Oszwald M et al (2008) Robot assisted fracture reduction. Experimental robotics, Springer tracts in advanced robotics, vol 39, pp 153–163. doi:10.1007/978-3-540-77457-0_15

  18. Westphal R, Winkelbach S, Gösling T et al (2008) Telemanipulated long bone fracture reduction. In: Bozovic V (ed) Medical robotics. I-Tech Education and Publishing, Vienna, pp 507– 526

  19. Westphal R, Winkelbach S, Wahl F et al (2009) Robot-assisted long bone fracture reduction. Int J Robot Res 28(10):1259–1278

    Article  Google Scholar 

  20. Westphal R (2007) Sensor based surgical robotics. Contributions to robot assisted fracture reduction. Shaker, Aachen

    Google Scholar 

  21. Graham AE, Xie SQ, Aw KC et al (2006) Design of a parallel long bone fracture reduction robot with planning treatment tool. In: Proceedings of the 2006 IEEE/RSJ; international conference on intelligent robots and systems, pp 1255–1260

  22. Graham AE, Xie SQ, Aw KC et al (2007) Robotic long bone fracture reduction. In: Bozovic V (ed) Medical robotics. I-Tech Education and Publishing, Vienna, pp 85–102

  23. Joung S, Kamon H, Liao H et al (2008) A robot assisted hip fracture reduction with a navigation system. In: Medical image computing and computer-assisted intervention, MICCAI 2008. Springer, pp 501–508

  24. Ye R, Chen Y (2009) Development of a six degree of freedom (DOF) hybrid robot for femur shaft fracture reduction. In: Proceedings of the 2008 IEEE, international conference on robotics and biomimetics, pp 306–311

  25. Warisawa S, Ishizuka T, Mitsuishi M et al (2004) Development of a femur fracture reduction robot. In: IEEE international conference on robotics and automation, vol 4. IEEE, Piscataway, NJ, pp 3999–4004

  26. Füchtmeier B, Egersdoerfer S, Mai R et al (2004) Reduction of femoral shaft fractures in vitro by a new developed reduction robot system ‘RepoRobo’. Injury 35(1):113–119. doi:10.1016/j.injury.2004.05.019

  27. Mukherjee S, Rendsburg M, Xu WL (2005) Surgeon-instructed, image-guided and robot-assisted long bone fractures reduction. In: 1st international conference on sensing technology, pp 78–84

  28. Seide K, Faschingbauer M, Wenzl ME et al (2004) A hexapod robot external fixator for computer assisted fracture reduction and deformity correction. IJMRCAS 01(01):64–69. doi:10.1581/mrcas.2004.010101

    Article  CAS  Google Scholar 

  29. Majidi Fakhr K, Kazemirad S, Farahmand F (2009) Robotic assisted reduction of femoral shaft fractures using stewart platform. Stud Health Technol Inform 142:177–179

    Google Scholar 

  30. Joung S, Shikh SS, Kobayashi E et al (2011) Musculoskeletal model of hip fracture for safety assurance of reduction path in robot-assisted fracture reduction. In: Abu-Osman NA, Ting H (eds) 5th Kuala Lumpur international conference on biomedical engineering, 20–23 June 2011, Kuala Lumpur, Malaysia, vol 35. Springer, Berlin, pp 116–120

  31. Kristen A, Culemann U, Fremd R et al (2008) Visualisierung von repositionspfaden. Unfallchirurg 111(6):395–402. doi:10.1007/s00113-008-1429-5

    Article  CAS  PubMed  Google Scholar 

  32. Lorensen WE, Cline HE (1987) Marching cubes: a high resolution 3d surface construction algorithm. SIGGRAPH Comput. Graph. 21(4):163–169. doi:10.1145/37402.37422

    Article  Google Scholar 

  33. Lohmann G (1998) Volumetric image analysis. Wiley, Teubner

    Google Scholar 

  34. Ohtake Y, Belyaev A, Seidel H (2004) Ridge-valley lines on meshes via implicit surface fitting. ACM Trans. Graph. 23(3):609. doi:10.1145/1015706.1015768

    Article  Google Scholar 

  35. Kühnel W (2008) Differentialgeometrie. Kurven-Flächen-Mannigfaltigkeiten, 4th edn. Vieweg, Wiesbaden

  36. Carmo MPd (1976) Differential geometry of curves and surfaces. Prentice-Hall, Upper Saddle River

    Google Scholar 

  37. Monga O, Benayoun S (1995) Using partial derivatives of 3D images to extract typical surface features. Comput vis image underst 61(2):171–189. doi:10.1006/cviu.1995.1014

  38. Therion J (1993) The extremal mesh and the understanding of 3D surfaces. Rapport de recherche, Institut National de Recherche en Informatique et en Automatique, vol 2149. INRIA, Le Chesnay

  39. Lengagne R, Tarel J, Monga O (1996) From 2D images to 3D face geometry. In: Proceedings of the second international conference on automatic face and gesture recognition, Oct 14–16 1996, Killington. Vermont. IEEE Computer Society Press, Los Alamitos, pp 301–306

  40. Kim S (2012) Extraction of ridge and valley lines from unorganized points. Multimed Tools Appl 63(1):265–279. doi:10.1007/s11042-012-0999-y

    Article  Google Scholar 

  41. Besl PJ, McKay ND, Schenker PS (1992) A method for registration 3-d shapes. IEEE Trans Pattern Anal Mach Intell 14(2):586–606. doi:10.1117/12.57955

    Google Scholar 

  42. Guéziec A, Pennec X, Ayache N (1997) Medical image registration using geometric hashing. IEEE Comput Sci Eng 4(4):29–41. doi:10.1109/99.641607

    Article  Google Scholar 

  43. Guéziec A, Ayache N (1991) Smoothing and matching of 3-D space curves. Rapports de recherche. Institut National de Recherche en Informatique et en Automatique, vol 1544. Institut National de Recherche en Informatique et en Automatique, Le Chesnay

  44. Pajdla T, van Gool L (1995) Matching of 3-D curves using semi-differential invariants. In: 5th international conference on computer vision, IEEE Computer Society Press, pp 390–395

  45. Pottmann H, Wallner J, Huang Q et al (2009) Integral invariants for robust geometry processing. Comput Aided Geom Des 26(1):37–60. doi:10.1016/j.cagd.2008.01.002

    Article  Google Scholar 

  46. Siciliano B, Khatib O (2008) Springer handbook of robotics. Springer, Berlin

    Book  Google Scholar 

  47. Forsyth DA, Ponce J (2012) Computer vision. A modern approach, 2nd edn. Pearson, Boston

  48. Winkelbach S (2006) Das 3d-puzzle-problem. Effiziente Methoden zum paarweisen Zusammensetzen von dreidimensionalen Fragmenten. Fortschritte in der Robotik, vol 10. Shaker, Aachen

  49. Winkelbach S, Westphal R, Goesling T (2003) Pose estimation of cylindrical fragments for semi-automatic bone fracture reduction. In: Krell G, Michaelis B (eds) Pattern recognition, proceedings. Magdeburg, 10–12 Sept 2003, vol 2781. Springer, Berlin, pp 566–573

  50. Winkelbach S, Rilk M, Schönfelder C et al (2004) Fast random sample matching of 3D fragments. In: Rasmussen CE (ed) Pattern Recognition. 26th DAGM Symposium, Tübingen, Germany, August 30-September 1, 2004: proceedings, vol 3175. Springer, Berlin, New York, pp 129–136

  51. Joskowicz L, Kronman A (2013) Automatic bone fracture reduction by fracture contact surface identification and registration. In: IEEE 10th international symposium on biomedical imaging, pp 246–249

  52. Moghari MH, Abolmaesumi P (2008) Global registration of multiple bone fragments using statistical atlas models: feasibility experiments. In: Dumont G, Galiana H, Proncipe J et al (eds) Proceedings of the 30th annual international conference of the IEEE Engineering in Medicine and Biology Society. “Personalized Healthcare through Technology”; 20–24 Aug 2008, Vancouver Convention & Exhibition Centre, Vancouver, BC. IEEE Operations Center, Piscataway, NJ, pp 5374–5377

  53. Albrecht T, Vetter T (2012) Automatic fracture reduction. In: Mesh processing in medical image analysis, vol 7599. Springer, pp 22–29

Download references

Acknowledgments

This research project is a cooperative project between the Department of Trauma, Hand and Reconstructive Surgery of the University Hospital of the Saarland and the University of Applied Sciences Kaiserslautern. Our thanks go to both partners, for technical and financial support. The research project is currently funded by the “Stiftung Rheinland-Pfalz für Innovation.”

Conflict of interest

Jan Buschbaum, Rainer Fremd, Tim Pohlemann and Alexander Kristen declare that they have no conflict of interest.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jan Buschbaum.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Buschbaum, J., Fremd, R., Pohlemann, T. et al. Computer-assisted fracture reduction: a new approach for repositioning femoral fractures and planning reduction paths. Int J CARS 10, 149–159 (2015). https://doi.org/10.1007/s11548-014-1011-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-014-1011-2

Keywords

Navigation