4D Virtual Imaging Platform for Dynamic Joint Assessment via Uni-Plane X-ray and 2D-3D Registration

2025-08-22

AI Summary

Conventional CT lacks capability for weight-bearing, dynamic joint functional assessment, while existing 4D imaging techniques suffer from either high radiation dose or insufficient spatial information. To address these limitations, this study proposes an integrated low-dose 4D joint analysis platform: a frameless dual-robotic-arm cone-beam CT system employing programmable scanning trajectories and a hybrid imaging protocol—enabling, for the first time, efficient fusion of upright static 3D and dynamic 2D acquisitions. Furthermore, we develop a deep learning–driven 2D–3D registration framework incorporating 3D-to-2D projection modeling and iterative optimization. Simulation results achieve sub-voxel accuracy of 0.235 mm (success rate: 99.18%). Clinical validation demonstrates accurate quantification of three-dimensional tibial plateau motion—including medial–lateral translation—in total knee arthroplasty patients, establishing a novel paradigm for postoperative biomechanical functional assessment.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: 3D Computer Vision
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Web Mining and Content Analysis: Web data integration and cleaning

Abstract

Conventional computed tomography (CT) lacks the ability to capture dynamic, weight-bearing joint motion. Functional evaluation, particularly after surgical intervention, requires four-dimensional (4D) imaging, but current methods are limited by excessive radiation exposure or incomplete spatial information from 2D techniques. We propose an integrated 4D joint analysis platform that combines: (1) a dual robotic arm cone-beam CT (CBCT) system with a programmable, gantry-free trajectory optimized for upright scanning; (2) a hybrid imaging pipeline that fuses static 3D CBCT with dynamic 2D X-rays using deep learning-based preprocessing, 3D-2D projection, and iterative optimization; and (3) a clinically validated framework for quantitative kinematic assessment. In simulation studies, the method achieved sub-voxel accuracy (0.235 mm) with a 99.18 percent success rate, outperforming conventional and state-of-the-art registration approaches. Clinical evaluation further demonstrated accurate quantification of tibial plateau motion and medial-lateral variance in post-total knee arthroplasty (TKA) patients. This 4D CBCT platform enables fast, accurate, and low-dose dynamic joint imaging, offering new opportunities for biomechanical research, precision diagnostics, and personalized orthopedic care.

Problem

• Lack of dynamic weight-bearing joint motion capture in CT
• Limitations in 4D imaging due to radiation or incomplete data
• Need for accurate kinematic assessment after surgical interventions

Innovation

• Dual robotic CBCT system with upright scanning
• Hybrid imaging fuses 3D CBCT with 2D X-rays
• Deep learning preprocessing and iterative optimization