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Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

The brain undergoes significant structural change over the course of neurosurgery, including highly nonlinear deformation and resection. It can be informative to recover the spatial mapping between structures identified in preoperative surgical planning and the intraoperative state of the brain. We present a novel feature-based method for achieving robust, fully automatic deformable registration of intraoperative neurosurgical ultrasound images.

Methods

A sparse set of local image feature correspondences is first estimated between ultrasound image pairs, after which rigid, affine and thin-plate spline models are used to estimate dense mappings throughout the image. Correspondences are derived from 3D features, distinctive generic image patterns that are automatically extracted from 3D ultrasound images and characterized in terms of their geometry (i.e., location, scale, and orientation) and a descriptor of local image appearance. Feature correspondences between ultrasound images are achieved based on a nearest-neighbor descriptor matching and probabilistic voting model similar to the Hough transform.

Results

Experiments demonstrate our method on intraoperative ultrasound images acquired before and after opening of the dura mater, during resection and after resection in nine clinical cases. A total of 1620 automatically extracted 3D feature correspondences were manually validated by eleven experts and used to guide the registration. Then, using manually labeled corresponding landmarks in the pre- and post-resection ultrasound images, we show that our feature-based registration reduces the mean target registration error from an initial value of 3.3 to 1.5 mm.

Conclusions

This result demonstrates that the 3D features promise to offer a robust and accurate solution for 3D ultrasound registration and to correct for brain shift in image-guided neurosurgery.

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Funding

This study is supported by the National Institute of Health Grants P41-EB015898-09, P41-EB015902 and R01-NS049251 and the Natural Sciences and Engineering Research Council of Canada, Discovery grant. The authors would like to acknowledge the financial support from the Portuguese Foundation for Science and Technology under the references PD/BD/105869/2014 and IDMEC/LAETA UID/EMS/50022/2013.

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Authors and Affiliations

  1. Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St., Boston, MA, 02115, USA

    Inês Machado, Jie Luo, Elizabeth George, Steve Pieper, Sarah Frisken & William Wells III

  2. IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal

    Inês Machado, Pedro Teodoro & Jorge Martins

  3. École de Technologie Superieure, 1100 Notre-Dame St W, Montreal, QC, H3C 1K3, Canada

    Matthew Toews

  4. Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, Japan

    Jie Luo

  5. Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St., Boston, MA, 02115, USA

    Prashin Unadkat, Walid Essayed & Alexandra Golby

  6. Department of Neurosurgery, CHLN, Hospital de Santa Maria, Avenida Professor Egas Moniz, 1649-035, Lisbon, Portugal

    Herculano Carvalho

  7. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA, 02139, USA

    Polina Golland & William Wells III

  8. Isomics, Inc., 55 Kirkland St, Cambridge, MA, 02138, USA

    Steve Pieper

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  1. Inês Machado
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Corresponding author

Correspondence to Inês Machado.

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Machado, I., Toews, M., Luo, J. et al. Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching. Int J CARS 13, 1525–1538 (2018). https://doi.org/10.1007/s11548-018-1786-7

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  • DOI: https://doi.org/10.1007/s11548-018-1786-7

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