# Pedicle Screw Pairing and Registration for Screw Pose Estimation from Dual C-arm Images Using CAD Models

arXiv: https://arxiv.org/abs/2511.05702 · HTML: https://mlsanity.com/paper/2511.05702/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 7 Nov 2025.

## Builds on (most cited first, 3 of 3)

- [DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedures](https://mlsanity.com/paper/1803.08606/) (2018)
- [Fast Auto-Differentiable Digitally Reconstructed Radiographs for Solving Inverse Problems in Intraoperative Imaging](https://mlsanity.com/paper/2208.12737/) (2022)
- [Regression-based Pelvic Pose Initialization for Fast and Robust 2D/3D Pelvis Registration](https://mlsanity.com/paper/2503.07767/) (2025)

## Built on by (most cited first, 2 of 2)

- [Intrinsic Tolerance in C-Arm Imaging: How Extrinsic Re-optimization Preserves 3D Reconstruction Accuracy](https://mlsanity.com/paper/2603.14031/) (2026)
- [Projection-Domain Sensitivity Analysis of Vertebral DRRs Under Intrinsic Calibration Perturbation](https://mlsanity.com/paper/2607.10551/) (2026)

## Abstract (arXiv)

Accurate matching of pedicle screws in both anteroposterior (AP) and lateral (LAT) images is critical for successful spinal decompression and stabilization during surgery. However, establishing screw correspondence, especially in LAT views, remains a significant clinical challenge. This paper introduces a method to address pedicle screw correspondence and pose estimation from dual C-arm images. By comparing screw combinations, the approach demonstrates consistent accuracy in both pairing and registration tasks. The method also employs 2D-3D alignment with screw CAD 3D models to accurately pair and estimate screw pose from dual views. Our results show that the correct screw combination consistently outperforms incorrect pairings across all test cases, even prior to registration. After registration, the correct combination further enhances alignment between projections and images, significantly reducing projection error. This approach shows promise for improving surgical outcomes in spinal procedures by providing reliable feedback on screw positioning.