Sun Wenyuan, Zou Xiaoyang, Zheng Guoyan
Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai, 200240, China.
Int J Comput Assist Radiol Surg. 2025 Jan;20(1):43-55. doi: 10.1007/s11548-024-03162-7. Epub 2024 May 10.
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.
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.
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 pixels. Additionally, the proposed C-arm calibration approach achieved a mean re-projection error of pixels and a mean point-to-line distance of mm. When the proposed C-arm calibration approach was applied to downstream tasks involving landmark and surface model reconstruction, sub-millimeter accuracy was achieved.
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.
使用可移动基准笼进行在线C形臂校准在各种图像引导介入手术中起着至关重要的作用。然而，开发一种全自动方法具有挑战性，这不仅需要精确检测基准投影，还需要稳健地建立二维与三维对应关系。
我们提出了一种使用可移动基准笼进行在线C形臂校准的新方法。具体而言，设计了一种嵌入16个基准点的新型可移动校准笼，其中基准点被布置成形成具有不同交比的4条线图案。然后，提出了一种基于自动上下文的检测网络（ADNet），以对采集的C形臂图像中的那些基准点的二维投影进行准确而稳健的检测。随后，我们提出了一种基于交比一致性的二维与三维对应关系建立方法，以自动将检测到的二维基准投影与那些三维基准点进行匹配，从而实现准确的在线C形臂校准。
我们设计并进行了全面的实验来评估所提出的方法。对于二维基准投影的自动检测，所提出的ADNet实现了平均点对点距离为 像素。此外，所提出的C形臂校准方法实现了平均重投影误差为 像素和平均点到线距离为 毫米。当将所提出的C形臂校准方法应用于涉及地标和表面模型重建的下游任务时，实现了亚毫米级的精度。
总之，我们开发了一种用于在线C形臂校准的新方法。全面实验的定性和定量结果都证明了所提出方法的准确性和稳健性。我们的方法在各种图像引导介入手术中具有潜力。
