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

Purpose. An algorithm for automatic spinal pedicle screw planning is reported and evaluated in simulation and first clinical studies.a#13; Methods. 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. a#13; Results. 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 (10 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. a#13; Conclusions. 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. a#13;