A Multi-modal Brain Image Registration Framework for US-guided Neuronavigation Systems - Integrating MR and US for Minimally Invasive Neuroimaging

Francesco Ponzio, Enrico Macii, Elisa Ficarra, Santa Di Cataldo

Abstract

US-guided neuronavigation exploits the simplicity of use and minimal invasiveness of Ultrasound (US) imaging and the high tissue resolution and signal-to-noise ratio of Magnetic Resonance Imaging (MRI) to guide brain surgeries. More specifically, the intra-operative 3D US images are combined with pre-operative MR images to accurately localise the course of instruments in the operative field with minimal invasiveness. Multimodal image registration of 3D US and MR images is an essential part of such system. In this paper, we present a complete software framework that enables the registration US and MR brain scans based on a multi resolution deformable transform, tackling elastic deformations (i.e. brain shifts) possibly occurring during the surgical procedure. The framework supports also simpler and faster registration techniques, based on rigid or affine transforms, and enables the interactive visualisation and rendering of the overlaid US and MRI volumes. The registration was experimentally validated on a public dataset of realistic brain phantom images, at different levels of artificially induced deformations.

References

  1. C. Nikas, D., Hartov, A., Lunn, K., Rick, K., Paulsen, K., and W. Roberts, D. (2003). Coregistered intraoperative ultrasonography in resection of malignant glioma. Neurosurgical Focus, 14(2):1-5.
  2. Chen, S. J.-S., Hellier, P., Gauvrit, J.-Y., Marchal, M., Morandi, X., and Collins, D. L. (2010). An Anthropomorphic Polyvinyl Alcohol Triple-Modality Brain Phantom Based on Colin27, pages 92-100. Springer Berlin Heidelberg, Berlin, Heidelberg.
  3. Coupé, P., Hellier, P., Morandi, X., and Barillot, C. (2012). 3d rigid registration of intraoperative ultrasound and preoperative mr brain images based on hyperechogenic structures. Journal of Biomedical Imaging, 2012:1:1-1:1.
  4. Liu, Y., Kot, A., Drakopoulos, F., Yao, C., Fedorov, A., Enquobahrie, A., Clatz, O., and Chrisochoides, N. (2014). An itk implementation of a physics-based non-rigid registration method for brain deformation in image-guided neurosurgery. Frontiers in Neuroinformatics, 8:33.
  5. Lunn, K. E., Hartov, A., Hansen, E. W., Sun, H., Roberts, D. W., and Paulsen, K. D. (2001). A quantitative comparison of edges in 3d intraoperative ultrasound and preoperative mr images of the brain. In Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 7801, pages 1081-1090, London, UK, UK. Springer-Verlag.
  6. Mattes, D., Haynor, D. R., Vesselle, H., Lewellyn, T. K., and Eubank, W. (2001). Nonrigid multimodality image registration.
  7. Sarkar, A., Santiago, R. J., Smith, R., and Kassaee, A. (2005). Comparison of manual vs. automated multimodality (ct-mri) image registration for brain tumors. Medical Dosimetry, 30(1):20 - 24.
  8. Schroeder, W. J., Martin, K., and Lorensen, W. (2003). The visualization toolkit: An object-oriented approach to 3d graphics, third edition.
  9. Xu, R., Chen, Y.-W., Tang, S.-Y., Morikawa, S., and Kurumi, Y. (2008). Parzen-window based normalized mutual information for medical image registration.
  10. IEICE - Trans. Inf. Syst., E91-D(1):132-144.
  11. Yoo, T. S., Ackerman, M. J., and Lorensen, W. E. (2002). Engineering and algorithm design for an image processing API: A technical report on itk-the insight toolkit. Proc. of Medicine Meets Virtual Reality, pages 586-592.
Download


Paper Citation


in Harvard Style

Ponzio F., Macii E., Ficarra E. and Di Cataldo S. (2017). A Multi-modal Brain Image Registration Framework for US-guided Neuronavigation Systems - Integrating MR and US for Minimally Invasive Neuroimaging . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2017) ISBN 978-989-758-215-8, pages 114-121. DOI: 10.5220/0006239201140121


in Bibtex Style

@conference{bioimaging17,
author={Francesco Ponzio and Enrico Macii and Elisa Ficarra and Santa Di Cataldo},
title={A Multi-modal Brain Image Registration Framework for US-guided Neuronavigation Systems - Integrating MR and US for Minimally Invasive Neuroimaging},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2017)},
year={2017},
pages={114-121},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006239201140121},
isbn={978-989-758-215-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2017)
TI - A Multi-modal Brain Image Registration Framework for US-guided Neuronavigation Systems - Integrating MR and US for Minimally Invasive Neuroimaging
SN - 978-989-758-215-8
AU - Ponzio F.
AU - Macii E.
AU - Ficarra E.
AU - Di Cataldo S.
PY - 2017
SP - 114
EP - 121
DO - 10.5220/0006239201140121