Automatic trajectory and instrument planning for robot-assisted spine surgery

Rohan C. Vijayan, Tharindu S. De Silva, Runze Han, Ali Uneri, Sophia A. Doerr, Michael D. Ketcha, Alexander Perdomo-Pantoja, Nicholas Theodore, Jeffrey H. Siewerdsen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Purpose. We report the initial implementation of an algorithm that automatically plans screw trajectories for spinal pedicle screw placement procedures to improve the workflow, accuracy, and reproducibility of screw placement in freehand navigated and robot-assisted spinal pedicle screw surgery. In this work, we evaluate the sensitivity of the algorithm to the settings of key parameters in simulation studies. Methods. Statistical shape models (SSMs) of the lumbar spine were constructed with segmentations of L1-L5 and bilateral screw trajectories of N=40 patients. Active-shape model (ASM) registration was devised to map the SSMs to the patient CT, initialized simply by alignment of (automatically annotated) single-point vertebral centroids. The atlas was augmented by definition of "ideal/reference" trajectories for each spinal pedicle, and the trajectories are deformably mapped to the patient CT. A parameter sensitivity analysis for the ASM method was performed on 3 parameters to determine robust operating points for ASM registration. The ASM method was evaluated by calculating the root-mean-square-error between the registered SSM and the ground-truth segmentation for the L1 vertebra, and the trajectory planning method was evaluated by performing a leave-one-out analysis and determining the entry point, end point, and angular differences between the automatically planned trajectories and the neurosurgeon-defined reference trajectories. Results. The parameter sensitivity analysis showed that the ASM registration algorithm was relatively insensitive to initial profile length (PLinitial) less than ∼4 mm, above which runtime and registration error increased. Similarly stable performance was observed for a maximum number of principal components (PCmax) of at least 8. Registration error ∼2 mm was evident with diminishing return beyond a number of ITERations, NITER, ∼2000. With these parameter settings, ASM registration of L1 achieved (2.0 ± 0.5) mm RMSE. Transpedicle trajectories for L1 agreed with reference definition by (2.6 ± 1.3) mm at the entry point, by (3.4 ± 1.8) mm at the end point, and within (4.9° ±2.8°) in angle. Conclusions. Initial results suggest that the algorithm yields accurate definition of pedicle trajectories in unsegmented CT images of the spine. The studies identified stable operating points for key algorithm parameters and support ongoing development and translation to clinical studies in free-hand navigated and robot-assisted spine surgery, where fast, accurate trajectory definition is essential to workflow.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2019
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
EditorsBaowei Fei, Cristian A. Linte
PublisherSPIE
ISBN (Electronic)9781510625495
DOIs
StatePublished - Jan 1 2019
EventMedical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling - San Diego, United States
Duration: Feb 17 2019Feb 19 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10951
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling
CountryUnited States
CitySan Diego
Period2/17/192/19/19

Keywords

  • Atlas-based registration
  • Automatic planning
  • Image-guided surgery
  • Surgical data science
  • Surgical robotics

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Vijayan, R. C., De Silva, T. S., Han, R., Uneri, A., Doerr, S. A., Ketcha, M. D., Perdomo-Pantoja, A., Theodore, N., & Siewerdsen, J. H. (2019). Automatic trajectory and instrument planning for robot-assisted spine surgery. In B. Fei, & C. A. Linte (Eds.), Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling [1095102] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10951). SPIE. https://doi.org/10.1117/12.2513722