Computed tomography (CT) and cone-beam computed tomography (CBCT) image registration play pivotal roles in computer-assisted navigation for orthopedic surgery. Traditional methods often apply uniform deformation models, neglecting the biomechanical differences between rigid structures and soft tissues, which compromises registration accuracy, especially during significant bone displacements. To address this issue, we introduce RE-Reg, a rigid-elastic CT-CBCT image registration framework that jointly learns rigid bone motion and soft tissue deformation. RE-Reg incorporates a rigid alignment (RA) module to estimate global bone motion and an elastic deformation (ED) module to model soft tissue deformation, preserving bony structures through bone shape preservation (BSP) loss. Our comprehensive evaluation on publicly available datasets demonstrates that RE-Reg significantly outperforms existing methods in terms of registration accuracy and rigid bone structure preservation, achieving a 1.3% improvement in Dice similarity coefficient (DSC) and a 23% reduction in rigid bone deformation ( % Δ vol ) compared with the best baseline. This framework not only enhances anatomical fidelity but also ensures biomechanical plausibility and provides a valuable tool for image-guided orthopedic surgery. This code is available at https://github.com/Zq-Huang/RE-Reg.
HUANG et al. (Sun,) studied this question.