Automatic pedicle screw planning using atlas-based registration of anatomy and reference trajectories

Abstract

An algorithm for automatic spinal pedicle screw planning is reported and evaluated in simulation and first clinical studies. A statistical atlas of the lumbar spine (N = 40 members) was constructed for active shape model (ASM) registration of target vertebrae to an unsegmented patient CT. The atlas was augmented to include 'reference' trajectories through the pedicles as defined by a spinal neurosurgeon. Following ASM registration, the trajectories are transformed to the patient CT and accumulated to define a patient-specific screw trajectory, diameter, and length. The algorithm was evaluated in leave-one-out analysis (N = 40 members) and for the first time in a clinical study (N = 5 patients undergoing cone-beam CT (CBCT) guided spine surgery), and in simulated low-dose CBCT images. ASM registration achieved (2.0 ± 0.5) mm root-mean-square-error (RMSE) in surface registration in 96% of cases, with outliers owing to limitations in CT image quality (high noise/slice thickness). Trajectory centerlines were conformant to the pedicle in 95% of cases. For all non-breaching trajectories, automatically defined screw diameter and length were similarly conformant to the pedicle and vertebral body (98.7%, Grade A/B). The algorithm performed similarly in CBCT clinical studies (93% centerline and screw conformance) and was consistent at the lowest dose levels tested. Average runtime in planning five-level (lumbar) bilateral screws (ten trajectories) was (312.1 ± 104.0) s. The runtime per level for ASM registration was (41.2 ± 39.9) s, and the runtime per trajectory was (4.1 ± 0.8) s, suggesting a runtime of ~(45.3 ± 39.9) s with a more fully parallelized implementation. The algorithm demonstrated accurate, automatic definition of pedicle screw trajectories, diameter, and length in CT images of the spine without segmentation. The studies support translation to clinical studies in free-hand or robot-assisted spine surgery, quality assurance, and data analytics in which fast trajectory definition is a benefit to workflow.

Figure 1.

Illustration of the automatic trajectory planning algorithm. Terms are defined in table 1.

Figure 2.

(a) Illustration of the anatomical atlas. Mean shape for SSMs L1–L5 (top row) and deformation ±3 standard deviations from the mean shape along the first principal component (second and third row). (b) Illustration of the trajectory atlas. Visualizations of reference trajectories in the first member of the atlas (n = 1), for L1–L5.

Figure 3.

Illustration of components defined within a vertebral level. (a) Vertices belonging to the vertebral body and pedicle distinguished from those of the spinous process (L1 SSM is shown). (b) Sub-components within the pedicle corridor: medial, lateral, superior, and inferior walls (L1 left pedicle is shown). Also shown in (B) are pertinent distances from the centerline (green): medial (d_med,i,ped), lateral (d_lat,i,ped), superior (d_sup,i,ped), and inferior (d_inf,i,ped).

Figure 4.

Effect of atlas size and membership on registration accuracy. (a) Registration RMSE for atlases ranging from 10 to 39 members.

Figure 5.

Accuracy of ASM registration in the leave-one study over 40 (unsegmented) CT images. (a) Violin plots of average RMSE for each vertebral level. (b) Surface rendering of the surface distance error for each vertebral level. (c) Surface overlays for true vertebral surface (green) and registered surface (red).

Figure 6.

Trajectory centerline differences between automatically planned trajectories and atlas definitions. (a) Entry point differences (in mm). (b) Angular differences (in degrees).

Figure 7.

Conformance of automatically defined trajectory centerlines and screw plans within bone corridors in the leave-one-out study over 40 cases. (a) Heatmap probability distribution of the distance of the planned trajectory centerlines to the pedicle cortex as a function of distance along the trajectory. The solid blue line indicates the median distance, and the dashed blue lines indicate the interquartile range. Red markers indicate breaches of the cortical wall. (b) Violin plots of the minimum distance of planned screws to the medial, lateral, superior, and inferior cortical walls, along with GB classifications.

Figure 8.

Automatically determined pedicle screw plans (including 3D trajectory as well as screw diameter and length) for an example case reflecting median performance in the leave-one-out study.

Figure 9.

Accuracy of ASM registration in the CBCT study over 5 (unsegmented) CBCT images. (a) Violin plots of average RMSE for each vertebral level. (b) Surface rendering of the surface distance error for each vertebral level. (c) Surface overlays of the ground truth vertebral surface (green) and registered surface (red).

Figure 10.

Conformance of automatically planned trajectory centerlines and screws within bone corridors in the clinical CBCT study. (a) Heatmap probability distribution of the distance of the planned trajectory centerlines to the pedicle cortex as a function of distance along the trajectory. The solid blue line indicates the median distance, and the dashed blue lines indicate the interquartile range. Red markers indicate breaches of the cortical wall. (b) Violin plots of the minimum distance of planned screws to the medial, lateral, superior, and inferior cortical walls, along with GB classifications.

Figure 11.

Automatic planning in low-dose CBCT. (a) RMSE versus dose level for the low-dose CBCT study. Violin plots represent the distributions at each dose level for all 3 vertebrae, with a total of 5 runs performed for each vertebrae (3 vertebrae × 5 runs = 15 total runs). (b) Interval plot for left and right trajectories, for L3–L5, in the low-dose cone-beam CT study. The intervals represent the range of distance profiles across all 4 dose levels.