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.
Correlation Ratio (CR) Brain Shift Correction Mean Target Registration Error Brain Tumor Removal Image-guided Neurosurgery System (IGNS)
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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.
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
Gerard, I.J., et al.: Brain shift in neuronavigation of brain tumors: a review. Med. Image Anal. 35, 403–420 (2017)CrossRefGoogle Scholar
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
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
Roche, A., et al.: Multimodal image registration by maximization of the correlation ratio. Ph.D. thesis. INRIA (1998)Google Scholar
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
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
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