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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.
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