Advertisement

MARCEL (Inter-Modality Affine Registration with CorrELation Ratio): An Application for Brain Shift Correction in Ultrasound-Guided Brain Tumor Resection

  • Nima MasoumiEmail author
  • Yiming Xiao
  • Hassan Rivaz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10670)

Abstract

Tissue deformation during brain tumor removal often renders the original surgical plan invalid. This can greatly affect the quality of resection, and thus threaten the patient’s survival rate. Therefore, correction of such deformation is needed, which can be achieved through image registration between pre- and intra-operative images. We proposed a novel automatic inter-modal affine registration technique based on the correlation ratio (CR) similarity metric. The technique was demonstrated through registering intra-operative ultrasound (US) scans with magnetic resonance (MR) images of patients, who underwent brain gliomas resection. By using landmark-based mean target registration errors (TRE) for evaluation, our technique has achieved a result of 2.32 ± 0.68 mm from the initial 5.13 ± 2.78 mm.

Notes

Acknowledgement

This work is funded by Natural Science Engineering Council of Canada (NSERC) grant RGPIN-2015-04136. The authors would like to thank anonymous reviewers for their valuable feedback.

References

  1. 1.
    Holland, E.C.: Progenitor cells and glioma formation. Curr. Opin. Neurol. 14(6), 683–688 (2001)CrossRefGoogle Scholar
  2. 2.
    Dolecek, T.A., et al.: CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2005–2009. Neuro-Oncol. 14(suppl 5), v1–v49 (2012)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Gerard, I.J., et al.: Brain shift in neuronavigation of brain tumors: a review. Med. Image Anal. 35, 403–420 (2017)CrossRefGoogle Scholar
  4. 4.
    De Nigris, D., Collins, D.L., Arbel, T.: Multi-modal image registration based on gradient orientations of minimal uncertainty. IEEE Trans. Med. Imaging 31(12), 2343–2354 (2012)CrossRefGoogle Scholar
  5. 5.
    Xiao, Y., et al.: 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, 3875–3882 (2017)CrossRefGoogle Scholar
  6. 6.
    Roche, A., et al.: Multimodal image registration by maximization of the correlation ratio. Ph.D. thesis. INRIA (1998)Google Scholar
  7. 7.
    Rivaz, H., Chen, S.J.S., Collins, D.L.: Automatic deformable MR-ultrasound registration for image-guided neurosurgery. IEEE Trans. Med. Imaging 34(2), 366–380 (2015)CrossRefGoogle Scholar
  8. 8.
    Klein, S., Staring, M., Pluim, J.P.W.: Evaluation of optimization methods for nonrigid medical image registration using mutual information and B-splines. IEEE Trans. Image Process. 16(12), 2879–2890 (2007)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Rivaz, H., Collins, D.L.: Near real-time robust non-rigid registration of volumetric ultrasound images for neurosurgery. Ultrasound Med. Biol. 41(2), 574–587 (2015)CrossRefGoogle Scholar
  10. 10.
    Daga, P., et al.: Accurate localization of optic radiation during neurosurgery in an interventional MRI suite. IEEE Trans. Med. Imaging 31(4), 882–891 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.PERFORM CentreConcordia UniversityMontrealCanada
  2. 2.Department of Electrical and Computer EngineeringConcordia UniversityMontrealCanada

Personalised recommendations