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A fully automatic fiducial detection and correspondence establishing method for online C-arm calibration

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Online C-arm calibration with a mobile fiducial cage plays an essential role in various image-guided interventions. However, it is challenging to develop a fully automatic approach, which requires not only an accurate detection of fiducial projections but also a robust 2D–3D correspondence establishment.

Methods

We propose a novel approach for online C-arm calibration with a mobile fiducial cage. Specifically, a novel mobile calibration cage embedded with 16 fiducials is designed, where the fiducials are arranged to form 4 line patterns with different cross-ratios. Then, an auto-context-based detection network (ADNet) is proposed to perform an accurate and robust detection of 2D projections of those fiducials in acquired C-arm images. Subsequently, we present a cross-ratio consistency-based 2D–3D correspondence establishing method to automatically match the detected 2D fiducial projections with those 3D fiducials, allowing for an accurate online C-arm calibration.

Results

We designed and conducted comprehensive experiments to evaluate the proposed approach. For automatic detection of 2D fiducial projections, the proposed ADNet achieved a mean point-to-point distance of \(0.65 \pm 1.33\) pixels. Additionally, the proposed C-arm calibration approach achieved a mean re-projection error of \(1.01 \pm 0.63\) pixels and a mean point-to-line distance of \(0.22 \pm 0.12\) mm. When the proposed C-arm calibration approach was applied to downstream tasks involving landmark and surface model reconstruction, sub-millimeter accuracy was achieved.

Conclusion

In summary, we developed a novel approach for online C-arm calibration. Both qualitative and quantitative results of comprehensive experiments demonstrated the accuracy and robustness of the proposed approach. Our approach holds potentials for various image-guided interventions.

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Authors and Affiliations

  1. Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai, 200240, China

    Wenyuan Sun, Xiaoyang Zou & Guoyan Zheng

Authors
  1. Wenyuan Sun
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  2. Xiaoyang Zou
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  3. Guoyan Zheng
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Correspondence to Guoyan Zheng.

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This research was partially supported by the National Natural Science Foundation of China (U20A20199).

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Sun, W., Zou, X. & Zheng, G. A fully automatic fiducial detection and correspondence establishing method for online C-arm calibration. Int J CARS (2024). https://doi.org/10.1007/s11548-024-03162-7

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  • DOI: https://doi.org/10.1007/s11548-024-03162-7

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