A Hybrid 3D-2D Image Registration Framework for Pedicle Screw Trajectory Registration between Intraoperative X-ray Image and Preoperative CT Image

Abstract

Pedicle screw insertion is considered a complex surgery among Orthopaedics surgeons. Exclusively to prevent postoperative complications associated with pedicle screw insertion, various types of image intensity registration-based navigation systems have been developed. These systems are computation-intensive, have a small capture range and have local maxima issues. On the other hand, deep learning-based techniques lack registration generalizability and have data dependency. To overcome these limitations, a patient-specific hybrid 3D-2D registration principled framework was designed to map a pedicle screw trajectory between intraoperative X-ray image and preoperative CT image. An anatomical landmark-based 3D-2D Iterative Control Point (ICP) registration was performed to register a pedicular marker pose between the X-ray images and axial preoperative CT images. The registration framework was clinically validated by generating projection images possessing an optimal match with intraoperative X-ray images at the corresponding control point registration. The effectiveness of the registered trajectory was evaluated in terms of displacement and directional errors after reprojecting its position on 2D radiographic planes. The mean Euclidean distances for the Head and Tail end of the reprojected trajectory from the actual trajectory in the AP and lateral planes were shown to be 0.6–0.8 mm and 0.5–1.6 mm, respectively. Similarly, the corresponding mean directional errors were found to be 4.90 and 20. The mean trajectory length difference between the actual and registered trajectory was shown to be 2.67 mm. The approximate time required in the intraoperative environment to axially map the marker position for a single vertebra was found to be 3 min. Utilizing the markerless registration techniques, the designed framework functions like a screw navigation tool, and assures the quality of surgery being performed by limiting the need of postoperative CT.

A Review of 3D-2D Registration Methods and Applications based on Medical Images

“…The framework uses normalized gradient information as a similarity metric and refines the registration states using a covariance matrix adaptive evolution strategy [80], which improves the robustness and efficiency of the registration, but is not stable to deformation and content mismatch. To enhance the registration stability, Naik et al [81] proposed a 3D-2D registration framework applied to pedicle screw placement. The framework used ICP algorithm and DRR generation technique to align the pedicle screw trajectory between X-ray images and CT images, and then the validity of the registration trajectory was evaluated by displacement and orientation errors.…”

Feature-Based Registration Framework for Pedicle Screw Trajectory Registration Between Multimodal Images

“…Prior to the registration, the target vertebral region exposed to the pedicle screw insertion surgery is cropped from pre-operative MR images. After selecting the target vertebrae, the following procedure is employed to choose the suitable screw size and to plan the screw trajectory path for the target vertebrae [22]. The middle axial slice of the target vertebrae is identified, and the midline is drawn on the mid-axial slice from the anterior end to the posterior end such that the line axially divides the vertebrae symmetrically.…”

“…The pedicle cross point is identified by viewing the pedicle's center point in a slice that has the widest pedicle in all the planes. The line joining the trajectory entry point, pedicle cross point and trajectory endpoint is known as Transverse Pedicular Angle (TPA) [22] for the screw trajectory plan. The trajectory is designed to allow for lateral initiation; as the probe is advanced, the axis is then pierced medially to the appropriate depth, passing through the pedicle cross point.…”

“…Naik et al, [22] designed an anatomical landmark-based 3D to 2D Iterative Control Point (ICP) registration to register pre-operative CT with intra-operative X-ray images to estimate the pedicular marker pose. The registration framework was validated by optimizing the projection image using the VOLUME 11, 2023 corresponding control point registration.…”

Evaluating similarity measure for multimodal 3D to 2D registration

“…The overlay of the 2D information onto the pre-operative images helps to determine the accurate vertebral pose in 3D view during spine surgery. Estimating the optimal transformation parameters between intra-operative 2D imaging geometry and the pre-operative 3D volume coordinate system is the main objective of the 3D to 2D registration technique [2,3]. The registration technique provides 3D information about the vertebral body with a limited number of intra-operative x-ray images disrupting standard clinical settings.…”