Rigid 2D–3D Registration in Medical Imaging: A Survey

Rigid 2D-3D image registration plays a critical role in modern image-guided interventions by enabling the alignment of intraoperative X-ray images with preoperative volumetric data such as computed tomography (CT). Accurate 2D-3D registration allows clinicians to localize anatomical structures in three-dimensional space while relying on fast and low-dose intraoperative imaging, which is essential for applications including orthopedic surgery, spine navigation, radiation therapy, and interventional radiology. Despite significant progress over the past two decades, achieving robust and accurate registration remains challenging due to factors such as limited imaging viewpoints, occlusions, imaging noise, and the large search space of rigid transformations. This paper provides a comprehensive survey of rigid 2D-3D registration methods with a particular focus on X-ray-to-CT alignment. We first introduce the mathematical formulation of the registration problem and present a taxonomy of existing approaches. We then review the key technical components underlying modern registration systems. In addition, we summarize commonly used datasets and evaluation protocols, discussing widely adopted metrics such as target registration error (TRE), pose error, and reprojection error. The survey also highlights representative clinical applications and analyzes the practical challenges that remain in real-world deployment, including robustness to imaging artifacts, variations in imaging dose, and real-time computational constraints. Finally, we discuss emerging research directions, such as differentiable rendering, deep learning based pose estimation, and multi-view registration frameworks, which are expected to further improve the accuracy, robustness, and clinical applicability of 2D-3D registration methods.