TY - GEN
T1 - Automatic trajectory and instrument planning for robot-assisted spine surgery
AU - Vijayan, Rohan C.
AU - De Silva, Tharindu S.
AU - Han, Runze
AU - Uneri, Ali
AU - Doerr, Sophia A.
AU - Ketcha, Michael D.
AU - Perdomo-Pantoja, Alexander
AU - Theodore, Nicholas
AU - Siewerdsen, Jeffrey H.
N1 - Funding Information:
The work was supported in part by NIH Grant R01-EB-017226 and the Malone Center for Engineering in Healthcare (Johns Hopkins University).
Publisher Copyright:
© 2019 SPIE.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Atlas-based registration
KW - Automatic planning
KW - Image-guided surgery
KW - Surgical data science
KW - Surgical robotics
UR - http://www.scopus.com/inward/record.url?scp=85067287368&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067287368&partnerID=8YFLogxK
U2 - 10.1117/12.2513722
DO - 10.1117/12.2513722
M3 - Conference contribution
AN - SCOPUS:85067287368
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2019
A2 - Fei, Baowei
A2 - Linte, Cristian A.
PB - SPIE
T2 - Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling
Y2 - 17 February 2019 through 19 February 2019
ER -