Twin-ViMReg: DXR driven synthetic dynamic Standing-CBCTs through Twin Vision Mamba-based 2D/3D registration

Abstract

Medical imaging of the knee joint under physiological weight bearing is crucial for diagnosing and analyzing knee lesions. Existing modalities have limitations: Standing Cone-Beam Computed Tomography (Standing-CBCT) provides high-resolution 3D data but with long acquisition time and only a single static view, while Dynamic X-ray Imaging (DXR) captures continuous motion but lacks 3D structural information. These limitations motivate the need for dynamic 3D knee generation through 2D/3D registration of Standing-CBCT and DXR. Anatomically, although the femur, patella, and tibia–fibula undergo rigid motion, the joint as a whole exhibits non-rigid behavior. Consequently, existing rigid or non-rigid 2D/3D registration methods fail to fully address this scenario. We propose Twin-ViMReg, a twin-stream 2D/3D registration framework for multiple correlated objects in the knee joint. It extends conventional 2D/3D registration paradigm by establishing a pair of twined sub-tasks. By introducing a Multi-Objective Spatial Transformation (MOST) module, it models inter-object correlations and enhances registration robustness. The Vision Mamba-based encoder also strengthens the representation capacity of the method. We used 1,500 simulated data pairs from 10 patients for training and 56 real data pairs from 3 patients for testing. Quantitative evaluation shows that the mean TRE reached 3.36 mm, the RSR was 8.93% higher than the SOTA methods. With an average computation time of 1.22 s per X-ray image, Twin-ViMReg enables efficient 2D/3D knee joint registration within seconds, making it a practical and promising solution.