Skip to main content

Advertisement

SpringerLink
Log in

Brain-shift compensation by non-rigid registration of intra-operative ultrasound images with preoperative MR images based on residual complexity

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

Abstract

Purpose

Compensation for brain shift is often necessary for image-guided neurosurgery, requiring registration of intra-operative ultrasound (US) images with preoperative magnetic resonance images (MRI). A new image similarity measure based on residual complexity (RC) to overcome challenges of registration of intra-operative US and preoperative MR images was developed and tested.

Method

A new two-stage method based on the matching echogenic structures such as sulci is achieved by optimizing the residual complexity value in the wavelet domain between the ultrasound image and the probabilistic map of the MR image. The proposed method is a compromise between feature-based and intensity-based approaches. Evaluation was performed using a specially designed brain phantom and an in vivo patient data set.

Result

The results of the phantom data set registration confirmed that the proposed objective function outperforms the accuracy of adapted RC for multi-modal cases by 48 %. The mean fiducial registration error reached 1.17 and 2.14 mm when the method was applied on phantom and clinical data sets, respectively.

Conclusion

This improved objective function based on RC in the wavelet domain enables accurate non-rigid multi-modal (US and MRI) image registration which is robust to noise. This technology is promising for compensation of intra-operative brain shift in neurosurgery.

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

Similar content being viewed by others

References

  1. Chandler WF et al (1982) Intraoperative use of real-time ultrasonography in neurosurgery. J Neurosurg 57:157–163

    Article  CAS  PubMed  Google Scholar 

  2. Dohrmann GJ, Rubin JM (1982) Intraoperative ultrasound imaging of the spinal cord: syringomyelia, cysts, and tumors—a preliminary report. Surg Neurol 18:395–399

    Article  CAS  PubMed  Google Scholar 

  3. Dohrmann G, Rubin J (1985) Dynamic intraoperative imaging and instrumentation of brain and spinal cord using ultrasound. Neurol Clin 3:425

    CAS  PubMed  Google Scholar 

  4. Auer L, Van Velthoven V (1990) Intraoperative ultrasound (US) imaging. Comparison of pathomorphological findings in US and CT. Acta Neurochirurg 104:84–95

    Article  CAS  Google Scholar 

  5. Van Velthoven V, Auer L (1990) Practical application of intraoperative ultrasound imaging. Acta Neurochirurg 105:5–13

    Article  Google Scholar 

  6. Hammoud MA et al (1996) Use of intraoperative ultrasound for localizing tumors and determining the extent of resection: a comparative study with magnetic resonance imaging. J Neurosurg 84:737–741

    Article  CAS  PubMed  Google Scholar 

  7. Koivukangas J et al (1986) Three-dimensional ultrasound imaging of brain for neurosurgery. Ann Clin Res 18:65

    PubMed  Google Scholar 

  8. Trobaugh JW et al (1994) Frameless stereotactic ultrasonography: method and applications. Comput Med Imaging Graph 18:235–246

    Article  CAS  PubMed  Google Scholar 

  9. Roberts DW et al (1998) Intraoperative brain shift and deformation: a quantitative analysis of cortical displacement in 28 cases. Neurosurgery 43:749

    Article  CAS  PubMed  Google Scholar 

  10. Roche A et al (2000) Generalized correlation ratio for rigid registration of 3D ultrasound with MR images. In: Delp SL, DiGioia AM, Jaramaz B (eds) Medical image computing and computer-assisted intervention–MICCAI 2000, vol 1935. Springer, Berlin, pp 567–577

  11. Reinertsen I et al (2007) Validation of vessel-based registration for correction of brain shift. Med Image Anal 11:374–388

    Article  CAS  PubMed  Google Scholar 

  12. Gronningsaeter A et al (1996) Ultrasound-guided neurosurgery: a feasibility study in the 3–30 MHz frequency range. Br J Neurosurg 10:161–168

    Article  CAS  PubMed  Google Scholar 

  13. Mitsui T et al (2011) Skin shift and its effect on navigation accuracy in image-guided neurosurgery. Radiol Phys Technol 4:37–42

    Article  PubMed  Google Scholar 

  14. Unsgaard G et al (2002) Neuronavigation by intraoperative three-dimensional ultrasound: initial experience during brain tumor resection. Neurosurgery 50:804

    Article  PubMed  Google Scholar 

  15. Letteboer MMJ et al (2003) Rigid registration of 3D ultrasound data of brain tumours. In: International congress series, pp 433–439

  16. Ji S et al (2008) Mutual-information-based image to patient re-registration using intraoperative ultrasound in image-guided neurosurgery. Med Phys 35:4612

    Article  PubMed Central  PubMed  Google Scholar 

  17. Ji S et al (2008) Mutual-information-corrected tumor displacement using intraoperative ultrasound for brain shift compensation in image-guided neurosurgery. In: Proceedings of SPIE Vol, pp 69182H–1

  18. Arbel T et al (2001) Automatic non-linear MRI-ultrasound registration for the correction of intra-operative brain deformations. Med Image Comput Comput Assist Interv MICCAI 2001:913–922

    Google Scholar 

  19. Nakajima S et al (1997) Use of cortical surface vessel registration for image-guided neurosurgery. Neurosurgery 40:1201–1210

    Article  CAS  PubMed  Google Scholar 

  20. Coupé P et al (2007) A probabilistic objective function for 3D rigid registration of intraoperative US and preoperative MR brain images. In: 4th IEEE international symposium on biomedical imaging: from nano to macro, 2007. ISBI 2007, pp 1320–1323

  21. Porter C et al (2001) Three-dimensional registration and fusion of ultrasound and MRI using major vessels as fiducial markers. IEEE Trans Med Imaging 20:354–359

    Article  CAS  PubMed  Google Scholar 

  22. Hong J, Hashizume M (2010) An effective point-based registration tool for surgical navigation. Surg Endosc 24:944–948

    Article  PubMed  Google Scholar 

  23. Chen SJ-S et al (2012) Validation of a hybrid Doppler ultrasound vessel-based registration algorithm for neurosurgery. Int J Comput Assist Radiol Surg 7:667–685

    Article  PubMed Central  PubMed  Google Scholar 

  24. Farnia P et al (2011) An efficient point based registration of intra-operative ultrasound images with MR images for computation of brain shift; A phantom study. In: Annual international conference of the IEEE Engineering in Medicine and Biology Society, EMBC, pp 8074–8077

  25. Farnia P et al (2012) On the performance of improved ICP algorithms for registration of intra-ultrasound with pre-MR images; a phantom study. In: Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp 4390–4393

  26. Wein W et al (2013) Global registration of ultrasound to mri using the LC2 metric for enabling neurosurgical guidance. In: Medical image computing and computer-assisted intervention-MICCAI 2013. Springer, pp 34–41

  27. Coupé P et al (2012) 3D rigid registration of intraoperative ultrasound and preoperative MR brain images based on hyperechogenic structures. J Biomed Imaging 2012:1

  28. Arbel T et al (2004) Automatic non-linear MRI-ultrasound registration for the correction of intra-operative brain deformations. Comput Aid Surg 9:123–136

    Article  Google Scholar 

  29. Wein W et al (2008) Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention. Med Image Anal 12:577–585

    Article  PubMed  Google Scholar 

  30. Mercier L et al (2012) Comparing two approaches to rigid registration of three-dimensional ultrasound and magnetic resonance images for neurosurgery. Int J Comput Ass Radiol Surg 7:125–136

    Article  Google Scholar 

  31. Maintz JBA et al (1996) Evaluation of ridge seeking operators for multimodality medical image matching. IEEE Trans Pattern Anal Mach Intell 18:353–365

    Article  Google Scholar 

  32. Myronenko A, Song X (2010) Intensity-based image registration by minimizing residual complexity. IEEE Trans Med Imaging 29:1882–1891

    Article  PubMed  Google Scholar 

  33. Mercier L et al (2012) Online database of clinical MR and ultrasound images of brain tumors. Med Phys 39:3253

    Article  PubMed  Google Scholar 

  34. Ahmadian A et al (2013) An efficient method for estimating soft tissue deformation based on intraoperative stereo image features and point-based registration. Int J Imaging Syst Technol 23:294–303

    Article  Google Scholar 

  35. Park JG, Lee C (2009) Skull stripping based on region growing for magnetic resonance brain images. Neuroimage 47:1394–1407

    Article  PubMed  Google Scholar 

  36. Coupé P et al (2008) An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans Med Imaging 27:425–441

    Article  PubMed Central  PubMed  Google Scholar 

  37. Le Goualher G et al (1999) Automated extraction and variability analysis of sulcal neuroanatomy. IEEE Trans Med Imaging 18:206–217

    Article  PubMed  Google Scholar 

  38. Coupé P et al (2009) Nonlocal means-based speckle filtering for ultrasound images. IEEE Trans Image Process 18:2221–2229

    Article  PubMed Central  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Image-Guided Intervention Group, Research Centre of Biomedical Technology and Robotics, RCBTR, Tehran University of Medical Sciences, Tehran, Iran

    P. Farnia, A. Ahmadian & N. D. Serej

  2. Medical Physics and Biomedical Engineering Department, Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran

    P. Farnia, A. Ahmadian & N. D. Serej

  3. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran

    T. Shabanian

  4. Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada

    J. Alirezaie

Authors
  1. P. Farnia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Ahmadian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. T. Shabanian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. N. D. Serej
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. J. Alirezaie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Ahmadian.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Farnia, P., Ahmadian, A., Shabanian, T. et al. Brain-shift compensation by non-rigid registration of intra-operative ultrasound images with preoperative MR images based on residual complexity. Int J CARS 10, 555–562 (2015). https://doi.org/10.1007/s11548-014-1098-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-014-1098-5

Keywords

Search

Navigation