南方医科大学学报

南方医科大学学报 ›› 2023, Vol. 43 ›› Issue (9): 1636-1643.doi: 10.12122/j.issn.1673-4254.2023.09.23

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基于正交视角X线图像重建的3D/2D配准方法

弥 佳，周宇佳，冯前进   

  1. 南方医科大学生物医学工程学院//广东省医学图像处理重点实验室//广东省医学成像与诊断技术工程实验室，广东 广州 510515
  • 出版日期:2023-09-20 发布日期:2023-09-28

A 3D/2D registration method based on reconstruction of orthogonal-view Xray images

MI Jia, ZHOU Yujia, FENG Qianjin   

  1. School of Biomedical Engineering, Southern Medical University//Guangdong Provincial Key Laboratory of Medical Image Processing//Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China
  • Online:2023-09-20 Published:2023-09-28

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摘要： 目的 研究一种用于脊柱外科手术导航中术前CT图像与术中X线图像的3D/2D配准方法。方法 本文构建了一个基于3D图像重建的3D/2D配准算法。该算法对2D正交视角X线图像进行3D图像重建，将问题转换为了3D/3D配准；结合了重建和配准两个任务构建端对端的框架，并最终在3D流形空间中度量测地距离完成配准。结果 我们在公开数据集CTSpine1k上进行了实验。在两个具有不同大小初始配准误差的测试集上进行测试，对于较小初始误差的数据，达到了0.115°±0.095°的旋转估计误差和0.144±0.124 mm的平移估计误差；对于较大初始误差的数据，达到了0.792°±0.659°的旋转估计误差和0.867±0.701 mm的平移估计误差。结论 本文提出的方法能在满足实时需求的同时，实现鲁棒、精确的3D/2D配准，有望进一步提高脊柱外科导航性能。

关键词: 3D/2D配准；手术导航；重建；深度学习

Abstract: Objective To establish a 3D/2D registration method for preoperative CT and intra-operative X-ray images in image-guided spine surgery. Methods We propose a 3D/2D registration algorithm based on 3D image reconstruction. The algorithm performs 3D image reconstruction of 2D orthogonal view X-ray images, thus converting the problem into 3D/3D registration. By constructing an end-to-end framework that combines the two tasks of reconstruction and registration, the geodesic distance is measured in the 3D manifold space to complete the registration. Results We conducted experiments on the public dataset CTSpine1k. The tests on two test sets with different initial registration errors showed that for data with small initial errors, the proposed algorithm achieved a rotation estimation error of 0.115±0.095° and a translation estimation error of 0.144±0.124 mm; for data with larger initial errors, a rotation estimation error of 0.792±0.659° and a translation estimation error of 0.867±0.701 mm were achieved. Conclusion The proposed method can achieve robust and accurate 3D/2D registration at a speed that meets real-time requirements to improve the performance of spine surgery navigation.

Key words: 3D/2D registration; surgery navigation; reconstruction; deep learning