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Registration of 3D freehand ultrasound to a bone model for orthopedic procedures of the forearm

  • Matija Ciganovic
  • Firat Ozdemir
  • Fabien Pean
  • Philipp Fuernstahl
  • Christine Tanner
  • Orcun Goksel
Original Article
  • 404 Downloads

Abstract

Purpose

For guidance of orthopedic surgery, the registration of preoperative images and corresponding surgical plans with the surgical setting can be of great value. Ultrasound (US) is an ideal modality for surgical guidance, as it is non-ionizing, real time, easy to use, and requires minimal (magnetic/radiation) safety limitations. By extracting bone surfaces from 3D freehand US and registering these to preoperative bone models, complementary information from these modalities can be fused and presented in the surgical realm.

Methods

A partial bone surface is extracted from US using phase symmetry and a factor graph-based approach. This is registered to the detailed 3D bone model, conventionally generated for preoperative planning, based on a proposed multi-initialization and surface-based scheme robust to partial surfaces.

Results

36 forearm US volumes acquired using a tracked US probe were independently registered to a 3D model of the radius, manually extracted from MRI. Given intraoperative time restrictions, a computationally efficient algorithm was determined based on a comparison of different approaches. For all 36 registrations, a mean (± SD) point-to-point surface distance of \(0.57\,(\pm \,0.08)\,\hbox {mm}\) was obtained from manual gold standard US bone annotations (not used during the registration) to the 3D bone model.

Conclusions

A registration framework based on the bone surface extraction from 3D freehand US and a subsequent fast, automatic surface alignment robust to single-sided view and large false-positive rates from US was shown to achieve registration accuracy feasible for practical orthopedic scenarios and a qualitative outcome indicating good visual image alignment.

Keywords

Tracked ultrasound Bone segmentation Iterative refinement 

Notes

Acknowledgements

This work was funded by the Swiss National Science Foundation (SNSF) and a Highly Specialized Medicine (HSM2) grant of the Canton of Zurich.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the provincial ethics committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Court-Brown CM, Caesar B (2006) Epidemiology of adult fractures: a review. Injury 37(8):691–697CrossRefPubMedGoogle Scholar
  2. 2.
    Nellans KW, Kowalski E, Chung KC (2012) The epidemiology of distal radius fractures. Hand Clin 28(2):113–125CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Chung KC, Spilson SV (2001) The frequency and epidemiology of hand and forearm fractures in the United States. J Hand Surg 26(5):908–915CrossRefGoogle Scholar
  4. 4.
    Rohling R, Gee A, Berman L (1999) A comparison of freehand three-dimensional ultrasound reconstruction techniques. Med Image Anal 3(4):339–359CrossRefPubMedGoogle Scholar
  5. 5.
    Flach B, Makhinya M, Goksel O (2016) PURE: panoramic ultrasound reconstruction by seamless stitching of volumes. In: MICCAI W SASHIMI, pp 75–84Google Scholar
  6. 6.
    Prager RW, Gee AH, Treece GM, Cash CJ, Berman LH (2003) Sensorless freehand 3-D ultrasound using regression of the echo intensity. Ultrasound Med Biol 29(3):437–446CrossRefPubMedGoogle Scholar
  7. 7.
    Penney GP, Barratt DC, Chan CSK, Slomczykowski M, Carter TJ, Edwards PJ, Hawkes DJ (2005) Cadaver validation of intensity-based ultrasound to CT registration. In: MICCAI, pp 1000–1007Google Scholar
  8. 8.
    Gill S, Mousavi P, Fichtinger G, Chen E, Boisvert J, Pichora D, Abolmaesumi P (2009) Biomechanically constrained groupwise US to CT registration of the lumbar spine. In: MICCAI, pp 803–810Google Scholar
  9. 9.
    Wein W, Khamene A, Clevert D, Kutter O, Navab N (2007) Simulation & fully auto-matic multimodal registration of medical ultrasound. In: MICCAI, pp 136–143Google Scholar
  10. 10.
    Hacihaliloglu I, Rasoulian A, Rohling RN, Abolmaesumi P (2014) Local phase tensor features for 3-D ultrasound to statistical shape + pose spine model registration. IEEE TMI 33:2167–2179Google Scholar
  11. 11.
    Taquee F, Goksel O, Mahdavi SS, Keyes M, Morris WJ, Spadinger I, Salcudean S (2012) Deformable prostate registration from MR and TRUS images using surface error driven FEM models. In: SPIE medical imaging, vol. 8316Google Scholar
  12. 12.
    Khallaghi S, Sánchez CA, Sun J, Imani F, Khale AKG, Goksel O, Rasoulian A, Romagnoli C, Abdi H, Chang S, Mousavi P, Fenster A, Ward A, Fels S, Abolmaesumi P (2015) Biomechanically constrained surface registration: application to MR-TRUS fusion for prostate interventions. IEEE TMI 34(11):2404–14Google Scholar
  13. 13.
    Besl PJ, McKay ND (1992) A method for registration of 3-D shapes. IEEE TPAMI 14(2):239–256CrossRefGoogle Scholar
  14. 14.
    Beitzel J, Ahmadi S, Karamalis A, Wein W, Navab N (2012) Ultrasound bone detection using patient-specific CT prior. 2664–2667Google Scholar
  15. 15.
    Kowal J, Amstutz C, Langlotz F, Talib H, Ballester MG (2007) Automated bone contour detection in ultrasound B-mode images for minimally invasive registration in computer-assisted surgeryan in vitro evaluation. IJCARS 3(4):341–348Google Scholar
  16. 16.
    Brounstein A, Hacihaliloglu I, Guy P, Hodgson A, Abugharbieh R (2011) Towards real-time 3D US to CT bone image registration using phase and curvature feature based GMM matching. In: MICCAI, pp 235–242Google Scholar
  17. 17.
    Jian B, Vemuri BC (2005) A robust algorithm for point set registration using mixture of Gaussians. In: ICCV, pp 1246–1251Google Scholar
  18. 18.
    Moghari MH, Abolmaesumi P (2007) Point-based rigid-body registration using an unscented Kalman filter. IEEE TMI 26(12):1708–1728Google Scholar
  19. 19.
    Echeverría R, Cortes C, Bertelsen A, Macia I, Ruiz Ó E, Flórez J (2016) Robust CT to US 3D–3D registration by using principal component analysis and Kalman filtering. In: Computational methods and clinical applications for spine imaging, pp 52–63Google Scholar
  20. 20.
    Behnami D, Sedghi A, Anas EMA, Rasoulian A, Seitel A, Lessoway V, Ungi T, Yen D, Osborn J, Mousavi P, Rohling R, Abolmaesumi P (2017) Model-based registration of preprocedure MR and intraprocedure us of the lumbar spine. IJCARS 12(6):973–982Google Scholar
  21. 21.
    Nagpal S, Abolmaesumi P, Rasoulian A, Ungi T, Hacihaliloglu I, Osborn J, Borschneck DP, Lessoway VA, Rohling RN, Mousavi P (2014) CT to US registration of the lumbar spine: a clinical feasibility study. In: IPCAI, pp 108–117Google Scholar
  22. 22.
    Johnson H, Harris G, Williams K (2007) BRAINSFit: mutual information registrations of whole-brain 3D images, using the insight toolkit. Insight J 57(1)Google Scholar
  23. 23.
    Song X, Myronenko A (2010) Point set registration: coherent point drift. IEEE TPAMI 32:2262–2275CrossRefGoogle Scholar
  24. 24.
    Foroughi P, Boctor E, Swartz MJ, Taylor RH, Fichtinger G (2007) P6D-2 ultrasound bone segmentation using dynamic programming. In: IEEE ultrasonics symposium, pp 2523–2526Google Scholar
  25. 25.
    Wein W, Karamalis A, Baumgartner A, Navab N (2015) Automatic bone detection and soft tissue aware ultrasound-CT registration for computer-aided orthopedic surgery. IJCARS 10(6):971–979Google Scholar
  26. 26.
    Karamalis A, Wein W, Klein T, Navab N (2012) Ultrasound confidence maps using random walks. Med Image Anal 16(6):1101–1112CrossRefPubMedGoogle Scholar
  27. 27.
    Hacihaliloglu I, Abugharbieh R, Hodgson A, Rohling RN (2009) Bone surface localization in ultrasound using image phase-based features. Ultrasound Med Biol 35(9):1475–1487CrossRefPubMedGoogle Scholar
  28. 28.
    Kovesi P (1999) Image features from phase congruency. Videre J Comput Vis Res 1(3):1–26Google Scholar
  29. 29.
    Ozdemir F, Ozkan E, Goksel O (2016) Graphical modeling of ultrasound propagation in tissue for automatic bone segmentation. In: MICCAI, pp 256–264Google Scholar
  30. 30.
    Chen Y, Medioni G (1992) Object modelling by registration of multiple range images. Image Vis Comput 10(3):145–155CrossRefGoogle Scholar
  31. 31.
    Lasso A, Heffter T, Rankin A, Pinter C, Ungi T, Fichtinger G (2014) Plus: open-source toolkit for ultrasound-guided intervention systems. IEEE TBME 61(10):2527–2537Google Scholar
  32. 32.
    Schumann S (2016) State of the art of ultrasound-based registration in computer assisted orthopedic interventions. In: Computational radiology for orthopaedic interventions. Springer, pp 271–297Google Scholar

Copyright information

© CARS 2018

Authors and Affiliations

  1. 1.Computer-Assisted Applications in Medicine (CAiM)ETH ZurichZurichSwitzerland
  2. 2.Computer Assisted Research and Development (CARD), Balgrist University HospitalUniversity of ZurichZurichSwitzerland

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