Numerous ongoing advances aim to improve the accuracy, safety, and efficacy of spine surgery, which often involves the placement of pedicle screws to stabilize the spine in treatment of deformity, degeneration, or trauma. Examples include the use of 3D surgical navigation and robot-assisted surgery as well as intraoperative imaging for operating room quality assurance (ORQA) and evaluation of the surgical product. An additional area of growing interest involves image analytics for data-intensive predictive modeling as a means of clinical decision support, patient selection, and optimal planning.
All of these advances require some form of reference plan – a reliable definition of how pedicle screws should be oriented in the spine, and what types of screws are best suited to patient-specific anatomy. In surgical navigation and robot-assisted surgery, such planning is the “roadmap” by which screws are to be placed. In ORQA and surgical data science, such planning gives a reference from which to analyze deviations in device placement that might affect the quality of the surgical product. Conventionally, planning is performed manually by the surgeon – introducing a time bottleneck that does not support broad utilization of these advanced technologies and is not scalable to data-intensive (“big data”) approaches.
A recent paper by Rohan Vijayan (PhD student in Biomedical Engineering at Johns Hopkins University) and coauthors in the I-STAR Lab reports a new algorithm to automatically compute spine surgery plans to obtain accurate, reproducible, definitions of pedicle screw trajectories and screw size. The method operates with a minimum of user interaction and does not require segmentation of the patient CT images. Instead, the algorithm uses a statistical atlas that describes not only the shape variations of vertebrae but also contains ideal reference screw trajectories within each member of the atlas. By computing a deformable 3D atlas-to-CT registration, the ideal trajectories can be precisely morphed to the patient CT. The atlas can even be customized to reflect individual preferences of a particular surgeon.
The algorithm was tested in IRB studies involving offline analysis of 40 cases, demonstrating accurate trajectory definition within 2.4 mm and 3.6 degrees. The algorithm also computes the maximum diameter of screw that can safely traverse the spinal pedicle, and the maximum length that will provide strong purchase in the vertebral body without breach. The algorithm was also shown to be accurate in intraoperative cone-beam CT (CBCT) such as that acquired with a mobile C-arm or O-arm and to be robust even with very low-dose imaging protocols.
The paper was published in Physics in Medicine and Biology, 2019
Vijayan RC, De Silva T, Han R, Zhang X, Uneri A, Doerr SA, Ketcha MD, Perdomo-Pantoja A, Theodore N, Siewerdsen JH, “Automatic Pedicle Screw Planning Using Atlas-Based Registration of Anatomy and Reference Trajectories.” Physics in Medicine and Biology, 2019, doi:10.1088/1361-6560/ab2d66