Jiashun Wang, Hao Tang, Zhan Wu, Yikun Zhang, Yan Xi, Yang Chen, Chunfeng Yang, Yixin Zhou, Hui Tang
{"title":"Twin-ViMReg: DXR driven synthetic dynamic Standing-CBCTs through Twin Vision Mamba-based 2D/3D registration.","authors":"Jiashun Wang, Hao Tang, Zhan Wu, Yikun Zhang, Yan Xi, Yang Chen, Chunfeng Yang, Yixin Zhou, Hui Tang","doi":"10.1016/j.compmedimag.2025.102648","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"125 ","pages":"102648"},"PeriodicalIF":4.9000,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computerized Medical Imaging and Graphics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1016/j.compmedimag.2025.102648","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.