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Biomechanically constrained groupwise ultrasound to CT registration of the lumbar spine

  • Sean Gill

      Affiliations

    • Queen’s University, Kingston, ON, Canada
  • ,
  • Purang Abolmaesumi

      Affiliations

    • Queen’s University, Kingston, ON, Canada
    • University of British Columbia, Vancouver, BC, Canada
  • ,
  • Gabor Fichtinger

      Affiliations

    • Queen’s University, Kingston, ON, Canada
  • ,
  • Jonathan Boisvert

      Affiliations

    • Institute for Information Technology, National Research Council Canada, Ottawa, ON, Canada
  • ,
  • David Pichora

      Affiliations

    • Kingston General Hospital, Kingston, ON, Canada
  • ,
  • Dan Borshneck

      Affiliations

    • Kingston General Hospital, Kingston, ON, Canada
  • ,
  • Parvin Mousavi

      Affiliations

    • Queen’s University, Kingston, ON, Canada
    • Corresponding Author InformationCorresponding author.

Received 11 September 2009; received in revised form 28 February 2010; accepted 11 July 2010. published online 05 August 2010.
Corrected Proof

Abstract 

We present a groupwise US to CT registration algorithm for guiding percutaneous spinal interventions. In addition, we introduce a comprehensive validation scheme that accounts for changes in the curvature of the spine between preoperative and intraoperative imaging. In our registration methodology, each vertebra in CT is treated as a sub-volume and transformed individually. A biomechanical model is used to constrain the displacement of the vertebrae relative to one another. The sub-volumes are then reconstructed into a single volume. During each iteration of registration, an US image is simulated from the reconstructed CT volume and an intensity-based similarity metric is calculated with the real US image. Validation studies are performed on CT and US images from a sheep cadaver, five patient-based phantoms designed to preserve realistic curvatures of the spine and a sixth patient-based phantom where the curvature of the spine is changed between preoperative and intraoperative imaging.

For datasets where the spine curve between two imaging modalities was artificially perturbed, the proposed methodology was able to register initial misalignments of up to 20mm with a success rate of 95%. For the phantom with a physical change in the curvature of the spine introduced between the US and CT datasets, the registration success rate was 98.5%. Finally, the registration success rate for the sheep cadaver with soft-tissue information was 87%. The results demonstrate that our algorithm allows for robust registration of US and CT datasets, regardless of a change in the patients pose between preoperative and intraoperative image acquisitions.

Keywords: Ultrasound, Spine, Multimodal registration, Biomechanical model

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PII: S1361-8415(10)00100-3

doi:10.1016/j.media.2010.07.008

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