Enhanced CT-CBCT image registration for orthopedic surgery: Integrating rigid-elastic motion models

TY - JOUR

T1 - Enhanced CT-CBCT image registration for orthopedic surgery

T2 - Integrating rigid-elastic motion models

AU - HUANG, Zhiqi

AU - XIAO, Deqiang

AU - LIU, Hongxun

AU - SHAO, Long

AU - AI, Danni

AU - FAN, Jingfan

AU - FU, Tianyu

AU - LIN, Yucong

AU - SONG, Hong

AU - YANG, Jian

N1 - Publisher Copyright: Copyright © 2026. Publishing services by Elsevier B.V.

PY - 2026/2

Y1 - 2026/2

N2 - Background 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. Method 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. Result 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 (%Δvol) compared with the best baseline. Conclusion 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.

AB - Background 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. Method 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. Result 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 (%Δvol) compared with the best baseline. Conclusion 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.

KW - CT-CBCT

KW - Elastic deformation

KW - Image registration

KW - Orthopedic surgery

KW - Rigid motion

UR - https://www.scopus.com/pages/publications/105032523234

U2 - 10.1016/j.vrih.2026.01.001

DO - 10.1016/j.vrih.2026.01.001

M3 - Article

AN - SCOPUS:105032523234

SN - 2096-5796

VL - 8

SP - 87

EP - 100

JO - Virtual Reality and Intelligent Hardware

JF - Virtual Reality and Intelligent Hardware

IS - 1

ER -