Automating C-arm Alignment for Standard Imaging Views in Orthopedic Procedures

Overview

This study presents a deep learning-based approach to automate C-arm positioning for standard orthopedic imaging views using only 2D fluoroscopic images. The method predicts 5 degrees of freedom pose updates directly from 2D projections, eliminating the need for preoperative CT scans or external tracking hardware. Validation on simulated and cadaver X-rays demonstrates its potential to improve accuracy and reduce radiation exposure.

Background

Mobile fluoroscopic imaging is critical in orthopedic and trauma surgery for guiding interventions and verifying fracture reduction and implant placement. Achieving correct standard projections is challenging due to patient variability and the invisibility of internal anatomy externally, often leading to incorrect views and increased risk of errors such as fracture malunion. Current methods rely on trial-and-error fluoroscopy, preoperative CT scans, or external tracking systems, which limit clinical applicability and increase radiation exposure. There is a need for automated, hardware-independent solutions to assist C-arm positioning efficiently and accurately.

Data Highlights

Rikli et al. reported that only 78.8% of lateral post-implant projection images were correctly classified as standard lateral views, with 10.3% and 7.4% of images being non-assessable for fracture reduction and implant position, respectively. The proposed CNN regression model predicts 5 DoF pose updates from 2D projections without additional hardware or preoperative scans. The approach was tested on two anatomies (proximal femur and spine) and two standard projections each, showing generalizability to unseen cadaver X-rays without retraining.

Key Findings

• The proposed CNN model predicts 5 DoF C-arm pose adjustments directly from 2D fluoroscopic images, bypassing the need for preoperative CT or external tracking.
• Training on simulated fluoroscopic images with ground truth pose labels enables accurate learning despite the under-constrained nature of 2D projections.
• The method was validated on two anatomically distinct regions (proximal femur and spine) and multiple standard projections, demonstrating versatility.
• Generalization to unseen cadaver X-rays without retraining indicates robustness to real-world imaging variability.
• Compared to existing approaches, this method reduces reliance on additional hardware and complex workflows, potentially decreasing radiation exposure and operator dependency.

Clinical Implications

This automated C-arm positioning approach can streamline intraoperative imaging by reducing trial-and-error fluoroscopy, thereby decreasing radiation exposure to patients and staff. By providing consistent standard projections without additional hardware or preoperative imaging, it may improve the accuracy of fracture reduction and implant placement assessments. Integration into clinical workflows could enhance efficiency and reduce operator-dependent variability in orthopedic procedures.

Conclusion

The study demonstrates that deep learning-based regression from 2D fluoroscopic images can effectively guide C-arm positioning for standard orthopedic views without extra hardware or preoperative scans. This approach holds promise for improving imaging accuracy and safety in orthopedic surgery.

References

1. Rikli et al. (Year) -- Retrospective assessment of lateral post-implant projection images
2. Miao et al. (Year) -- Deep learning-based C-arm localization methods
3. Bui et al. (Year) -- Machine learning-based pose estimation for mobile X-ray imaging
4. Haiderbhai et al. (Year) -- User interface for automatic C-arm positioning
5. Rodas et al. (Year) -- Monte Carlo approach to optimize C-arm pose