Skip to main content

Advertisement

SpringerLink
Log in

Multimodal 3D ultrasound and CT in image-guided spinal surgery: public database and new registration algorithms

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

Abstract

Purpose

Accurate multimodal registration of intraoperative ultrasound (US) and preoperative computed tomography (CT) is a challenging problem. Construction of public datasets of US and CT images can accelerate the development of such image registration techniques. This can help ensure the accuracy and safety of spinal surgeries using image-guided surgery systems where an image registration is employed. In addition, we present two algorithms to register US and CT images.

Methods

We present three different datasets of vertebrae with corresponding CT, US, and simulated US images. For each of the two latter datasets, we also provide 16 landmark pairs of matching structures between the CT and US images and performed fiducial registration to acquire a silver standard for assessing image registration. Besides, we proposed two patch-based rigid image registration algorithms, one based on normalized cross-correlation (NCC) and the other based on correlation ratio (CR) to register misaligned CT and US images.

Results

The CT and corresponding US images of the proposed database were pre-processed and misaligned with different error intervals, resulting in 6000 registration problems solved using both NCC and CR methods. Our results show that the methods were successful in aligning the pre-processed CT and US images by decreasing the warping index.

Conclusions

The database provides a resource for evaluating image registration techniques. The simulated data have two applications. First, they provide the gold standard ground-truth which is difficult to obtain with ex vivo and in vivo data for validating US-CT registration methods. Second, the simulated US images can be used to validate real-time US simulation methods. Besides, the proposed image registration techniques can be useful for developing methods in clinical application.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Gerard IJ, Kersten-Oertel M, Drouin S, Hall JA, Petrecca K, De Nigris D, Di Giovanni DA, Arbel T, Collins DL (2018) Combining intraoperative ultrasound brain shift correction and augmented reality visualizations: a pilot study of eight cases. J Med Imaging 5(2):021210

    Article  Google Scholar 

  2. Della Pepa GM, Sabatino G, Sturiale CL, Marchese E, Puca A, Olivi A, Albanese A (2018) Integration of real-time intraoperative contrast-enhanced ultrasound and color doppler ultrasound in the surgical treatment of spinal cord dural arteriovenous fistulas. World Neurosurg 112:138–142

    Article  Google Scholar 

  3. Ganau M, Syrmos N, Martin AR, Jiang F, Fehlings MG (2018) Intraoperative ultrasound in spine surgery: history, current applications, future developments. Quant Imaging Med Surg 8(3):261

    Article  Google Scholar 

  4. Boursier J-F, Fournet A, Bassanino J, Manassero M, Bedu A-S, Leperlier D (2018) Ultrasonography is more accurate than percutaneous palpation for identifying targeted thoracolumbar intervertebral disc spaces in dogs. Vet Radiol Ultras 59:749–757

    Article  Google Scholar 

  5. Wein W, Brunke S, Khamene A, Callstrom MR, Navab N (2008) Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention. Med Image Anal 12(5):577–585

    Article  Google Scholar 

  6. Mattausch O, Goksel O (2018) Image-based reconstruction of tissue scatterers using beam steering for ultrasound simulation. IEEE Trans Med Imaging 37(3):767–780

    Article  Google Scholar 

  7. Tuzer M, Türkay R, Boyman M, Acar B (2018) Multi-ray medical ultrasound simulation without explicit speckle modelling. Int J Comput Assist Radiol Surg 13:1009–1017

    Article  Google Scholar 

  8. Rubi P, Vera EF, Larrabide J, Calvo M, D’Amato J, Larrabide I (2017) Comparison of real-time ultrasound simulation models using abdominal CT images. In: 12th international symposium on medical information processing and analysis, vol 10160, p 1016009. International Society for Optics and Photonics

  9. Burger B, Bettinghausen S, Radle M, Hesser J (2013) Real-time GPU-based ultrasound simulation using deformable mesh models. IEEE Trans Med Imaging 32(3):609–618

    Article  Google Scholar 

  10. Yan CX, Goulet B, Pelletier J, Chen SJS, Tampieri D, Collins DL (2011) Towards accurate, robust and practical ultrasound-CT registration of vertebrae for image-guided spine surgery. Int J Comput Assist Radiol Surg 6(4):523–537

    Article  Google Scholar 

  11. Koo TK, Kwok WE (2016) Hierarchical CT to ultrasound registration of the lumbar spine: a comparison with other registration methods. Ann Biomed Eng 44(10):2887–2900

    Article  Google Scholar 

  12. Xiao Y, Fortin M, Unsgård G, Rivaz H, Reinertsen I (2017) Retrospective evaluation of cerebral tumors (resect): a clinical database of pre-operative MRI and intra-operative ultrasound in low-grade glioma surgeries. Med Phys 44(7):3875–3882

    Article  Google Scholar 

  13. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M (2013) The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26(6):1045–1057

    Article  Google Scholar 

  14. Roche C, Bonaccio E, Filippini J (2016) Radiology data from the cancer genome atlas sarcoma[TCGA-SARC] collection. Cancer Imaging Arch. https://doi.org/10.7937/K9/TCIA.2016.CX6YLSUX

  15. Karamalis A, Wein W, Navab N (2010) Fast ultrasound image simulation using the westervelt equation. In: International conference on medical image computing and computer-assisted intervention. Springer, vol 13. pp 243–250

  16. Gill S, Abolmaesumi P, Fichtinger G, Boisvert J, Pichora D, Borshneck D (2012) Biomechanically constrained groupwise ultrasound to CT registration of the lumbar spine. Med Image Anal 16(3):662–674

    Article  Google Scholar 

  17. Jaros J, Rendell AP, Treeby BE (2016) Full-wave nonlinear ultrasound simulation on distributed clusters with applications in high-intensity focused ultrasound. Int J High Perform Comput Appl 30(2):137–155

    Article  Google Scholar 

  18. Roche A, Malandain G, Pennec X, Ayache N (1998) The correlation ratio as a new similarity measure for multimodal image registration. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 1115–1124

  19. Rivaz H, Chen SJS, Collins DL (2015) Automatic deformable MR-ultrasound registration for image-guided neurosurgery. IEEE Trans Med Imaging 34(2):366–380

    Article  Google Scholar 

  20. Masoumi N, Xiao Y, Rivaz H (2019) Arena: inter-modality affine registration using evolutionary strategy. Int J Comput Assist Radiol Surg 14(3):441–450

    Article  Google Scholar 

  21. Boyd S, Boyd SP, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge

    Book  Google Scholar 

  22. Kendall JL, Faragher JP (2007) Ultrasound-guided central venous access: a homemade phantom for simulation. Can J Emerg Med 9(5):371–373

    Article  Google Scholar 

  23. Wilke HJ, Kettler A, Wenger KH, Claes LE (1997) Anatomy of the sheep spine and its comparison to the human spine. Anat Rec Off Publ Am Assoc Anat 247(4):542–555

    Article  CAS  Google Scholar 

  24. Jensen JA (1996) Field: a program for simulating ultrasound systems. In: 10th nordicbaltic conference on biomedical imaging, vol 4, supplement 1, part 1. Citeseer, pp 351–353

  25. Lasso A, Heffter T, Rankin A, Pinter C, Ungi T, Fichtinger G (2014) Plus: open-source toolkit for ultrasound-guided intervention systems. IEEE Trans Biomed Eng 61(10):2527–2537

    Article  Google Scholar 

  26. Ungi T, Lasso A, Fichtinger G (2016) Open-source platforms for navigated image-guided interventions. Med Image Anal 33:181–186

    Article  Google Scholar 

  27. Brendel B, Rick SWA, Stockheim M, Ermert H (2002) Registration of 3d CT and ultrasound datasets of the spine using bone structures. Comput Aided Surg 7(3):146–155

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (NSERC) and in part by the Regroupement Strategique en Microelectronique du Quebec.

Author information

Authors and Affiliations

  1. PERFORM Centre, Concordia University, Montreal, Canada

    Nima Masoumi, Habib Benali & Hassan Rivaz

  2. Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada

    Nima Masoumi, Clyde J. Belasso, M. Omair Ahmad, Habib Benali, Yiming Xiao & Hassan Rivaz

Authors
  1. Nima Masoumi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Clyde J. Belasso
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Omair Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Habib Benali
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yiming Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hassan Rivaz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nima Masoumi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standard

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

Informed consent

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

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 687 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Masoumi, N., Belasso, C.J., Ahmad, M.O. et al. Multimodal 3D ultrasound and CT in image-guided spinal surgery: public database and new registration algorithms. Int J CARS 16, 555–565 (2021). https://doi.org/10.1007/s11548-021-02323-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-021-02323-2

Keywords

Search

Navigation