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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Holland, E.C.: Progenitor cells and glioma formation. Curr. Opin. Neurol. 14(6), 683–688 (2001)
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)
Gerard, I.J., et al.: Brain shift in neuronavigation of brain tumors: a review. Med. Image Anal. 35, 403–420 (2017)
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)
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)
Roche, A., et al.: Multimodal image registration by maximization of the correlation ratio. Ph.D. thesis. INRIA (1998)
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)
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)
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)
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)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Masoumi, N., Xiao, Y., Rivaz, H. (2018). MARCEL (Inter-Modality Affine Registration with CorrELation Ratio): An Application for Brain Shift Correction in Ultrasound-Guided Brain Tumor Resection. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017. Lecture Notes in Computer Science(), vol 10670. Springer, Cham. https://doi.org/10.1007/978-3-319-75238-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-75238-9_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-75237-2
Online ISBN: 978-3-319-75238-9
eBook Packages: Computer ScienceComputer Science (R0)