In image-guided surgery (IGS) practice, combining intraoperative 2D X-ray images with preoperative 3D X-ray images from computed tomography (CT) enables the rapid and accurate localization of lesions, which allows for a more minimally invasive and efficient surgery, and also reduces the risk of secondary injuries to nerves and vessels. Conventional optimization-based methods for 2D X-ray and 3D CT matching are limited in speed and precision due to non-convex optimization spaces and a constrained searching range. Recently, deep learning (DL) approaches have demonstrated remarkable proficiency in solving complex nonlinear 2D–3D registration. In this paper, a fast and robust DL-based registration method is proposed that takes an intraoperative 2D X-ray image as input, compares it with the preoperative 3D CT, and outputs their relative pose in x, y, z and pitch, yaw, roll. The method employs a dual-channel Swin transformer feature extractor equipped with attention mechanisms and feature pyramid to facilitate the correlation between features of the 2D X-ray and anatomical pose of CT. Tests on three different regions of interest acquired from open-source datasets show that our method can achieve high pose estimation accuracy (mean rotation and translation error of 0.142° and 0.362 mm, respectively) in a short time (0.02 s). Robustness tests indicate that our proposed method can maintain zero registration failures across varying levels of noise. This generalizable learning-based 2D (X-ray) and 3D (CT) registration algorithm owns promising applications in surgical navigation, targeted radiotherapy, and other clinical operations, with substantial potential for enhancing the accuracy and efficiency of image-guided surgery.

Sports enthusiasts frequently experience knee ligament injuries. This study assessed their self-perceived knowledge, attitudes, and preventive practices re

It is often difficult for surgeons to accurately implant the cannulated screws for fixing femoral neck fractures under C-arm fluoroscopy. Our simulation study attempts to help surgeons identify the optimal positions of screws on anteroposterior and lateral radiographs. Softwares were used for 3-dimensional reconstruction and cross-sections construction of 60 femoral necks. The contours of all cross-sections along the axis of the femoral neck were stacked to identify a safe zone for screws placement. A simulation experiment was carried out with 3 circles of diameter 6.5 mm in the safe zone. The absolute and relative positions of every screw in the anteroposterior radiographs and lateral radiographs were measured. The morphologies of the safe zones for screws placement were anteriorly flat ellipses. The best dispersion was obtained with the inverted triangle shape in this zone: the inferior was positioned near the lower 1/10 on the anteroposterior view and near the anterior 3/5 on the lateral view. The anterosuperior and posteriosuperior screws were positioned near the upper 1/5 and 1/4, respectively, on the anteroposterior view, and near the anterior 1/10 and posterior 1/5, respectively, on the lateral view. Simulated screws implantation demonstrates the distribution characteristics of screws in anteroposterior and lateral radiographs. This can help decrease the risk of perforation and iatrogenic injury.