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

Image-Guided Navigation in Spine Surgery: From Historical Developments to Future Perspectives

Intraoperative navigation is critical during spine surgery to ensure accurate instrumentation placement. From the early era of fluoroscopy to the current advancement in robotics, spinal navigation has continued to evolve. By understanding the variations in system protocols and their respective usage in the operating room, the surgeon can use and maximize the potential of various image guidance options more effectively. At the same time, maintaining navigation accuracy throughout the procedure is of the utmost importance, which can be confirmed intraoperatively by using an internal fiducial marker, as demonstrated herein. This technology can reduce the need for revision surgeries, minimize postoperative complications, and enhance the overall efficiency of operating rooms.

CT2X-IRA: CT to x-ray image registration agent using domain-cross multi-scale-stride deep reinforcement learning

Objective.In computer-assisted minimally invasive surgery, the intraoperative x-ray image is enhanced by overlapping it with a preoperative CT volume to improve visualization of vital anatomical structures. Therefore, accurate and robust 3D/2D registration of CT volume and x-ray image is highly desired in clinical practices. However, previous registration methods were prone to initial misalignments and struggled with local minima, leading to issues of low accuracy and vulnerability.Approach.To improve registration performance, we propose a novel CT/x-ray image registration agent (CT2X-IRA) within a task-driven deep reinforcement learning framework, which contains three key strategies: (1) a multi-scale-stride learning mechanism provides multi-scale feature representation and flexible action step size, establishing fast and globally optimal convergence of the registration task. (2) A domain adaptation module reduces the domain gap between the x-ray image and digitally reconstructed radiograph projected from the CT volume, decreasing the sensitivity and uncertainty of the similarity measurement. (3) A weighted reward function facilitates CT2X-IRA in searching for the optimal transformation parameters, improving the estimation accuracy of out-of-plane transformation parameters under large initial misalignments.Main results.We evaluate the proposed CT2X-IRA on both the public and private clinical datasets, achieving target registration errors of 2.13 mm and 2.33 mm with the computation time of 1.5 s and 1.1 s, respectively, showing an accurate and fast workflow for CT/x-ray image rigid registration.Significance.The proposed CT2X-IRA obtains the accurate and robust 3D/2D registration of CT and x-ray images, suggesting its potential significance in clinical applications.

Evaluating similarity measure for multimodal 3D to 2D registration

The 3D to 2D registration technique in spine surgery is vital to aid surgeons in avoiding the wrong site surgery by estimating the vertebral pose. The vertebral poses are estimated by generating the spatial correspondence relationship between pre-operative MR with intra-operative x-ray images, then evaluated using a similarity measure. Different similarity measures are used in 3D to 2D registration techniques to assess the spatial correspondence between the pre-operative and intra-operative images. However, to evaluate the registration performance of the similarity measures, the proposed framework employs three different similarity measures: Binary Image Matching, Dice Coefficients, and Normalized Cross-correlation technique to compare the images based on pixel positions. The registration accuracy of the proposed similarity measures is compared based on the mean Target Registration Error, mean Iteration Times, and success rate. In the absence of simulated test images, the experiment is conducted on the simulated AP and Lateral test images. The experiment conducted on the simulated test images shows that all three similarity measures work well for the feature based 3D to 2D registration in that BIM gives better results. The experiment also indicates high registration accuracy when the initial displacements are varied up to ±20 mm and ±100of the translational and rotational parameters, respectively, for three similarity measures.