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Toward Virtual Modeling and Templating for Enhanced Spine Surgery Planning

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Spinal Imaging and Image Analysis

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 18))

Abstract

Traditional 2D images provide limited use for accurate planning of spine interventions, due to their inability to display the complex 3D spine anatomy and close proximity of nerve bundles and vascular structures that must be avoided during the procedure. We have developed a platform for spine surgery planning that employs standard of care 3D pre-operative images and enables oblique reformatting and 3D rendering of individual or multiple vertebrae, interactive templating, and placement of virtual pedicle implants into the patient-specific CT data. Here we propose a combined surrogate metric—the Fastening Strength—to provide estimates of the optimal implant selection and trajectory based on implant dimension and bone mineral density of the displaced bone substrate. We conducted a retrospective clinical study based on pre- and post-operative data from four patients who underwent procedures involving pedicle screw implantation. We assessed the retrospective plans against the post-operative imaging data according to implant dimension, mean voxel intensity of implant trajectory, and Fastening Strength and showed consistency between the proposed plans and the post-operative procedure outcome. Our preliminary studies have demonstrated the feasibility of the platform in assisting the surgeon with the selection of appropriate size implant and trajectory that optimizes Fastening Strength, given the intrinsic vertebral geometry and bone mineral density. Herein we describe the platform infrastructure and capabilities, present preliminary studies conducted to assess impact on typical instrumentation procedures, and share our initial clinical experience in employing the proposed tool for the planning of several complicated spinal correction procedures for which the traditional planning approaches proved insufficient. Lastly, we also disseminate on several clinical cases and their post-operative assessment for which the proposed platform was employed by the surgical team.

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Acknowledgments

The authors would like to thank all members of the Biomedical Imaging Resource who have helped with the development and implementation of this project, especially Alan Larson, Bruce Cameron, Phillip Edwards, and Dennis Hanson. Also, we would like to acknowledge our clinical collaborators for their continuous support: Dr. Paul M. Huddleston, Dr. Jonathan Morris, Dr. Jane Matsumoto, and Dr. Shyam Shridharani.

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Authors and Affiliations

  1. Biomedical Engineering and Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA

    Cristian A. Linte

  2. Biomedical Imaging Resource, Mayo Clinic, Rochester, MN, USA

    Kurt E. Augustine, Jon J. Camp, Richard A. Robb & David R. Holmes III

Authors
  1. Cristian A. Linte
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  2. Kurt E. Augustine
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  3. Jon J. Camp
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  4. Richard A. Robb
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  5. David R. Holmes III
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Corresponding author

Correspondence to Cristian A. Linte .

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Editors and Affiliations

  1. GE Healthcare and University of Western Ontario, London, Ontario, Canada

    Shuo Li

  2. Clinical Center, National Institutes of Health, Bethesda, Maryland, USA

    Jianhua Yao

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Linte, C.A., Augustine, K.E., Camp, J.J., Robb, R.A., Holmes III, D.R. (2015). Toward Virtual Modeling and Templating for Enhanced Spine Surgery Planning. In: Li, S., Yao, J. (eds) Spinal Imaging and Image Analysis. Lecture Notes in Computational Vision and Biomechanics, vol 18. Springer, Cham. https://doi.org/10.1007/978-3-319-12508-4_14

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  • DOI: https://doi.org/10.1007/978-3-319-12508-4_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12507-7

  • Online ISBN: 978-3-319-12508-4

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