2D/3D多模态医学图像配准算法研究

Research on 2D/3D multimodal medical image registration algorithm

2D/3D多模态配准在医学影像导航手术中起着重要作用,主要用于提供术前三维图像和术中二维图像的实时信息,帮助医生精准定位病灶,规划手术路径,从而提高手术的安全性和效率。提出了一种2D/3D多模态医学图像配准算法,首先利用Swin Transformer优秀的特征提取能力,构建了初始姿态估计模型,实现姿态参数的快速预测;接着,为了提升整个配准方法的鲁棒性,引入基于Grangeat关系的粗配准方法;最后设计了基于梯度下降的精配准模块,以提升整个配准过程的精确性,且在该模块将Sobel微分算子与归一化互相关结合,提升了参数优化过程中的灵敏度。实验结果表明,所提配准方法在正位和侧位配准中的误差满足配准要求,配准成功率有显著提升。

2D/3D multimodal alignment plays an important role in medical image navigation surgery,which is mainly used to provide realtime information of preoperative 3D images and intraoperative 2D images to help doctors accurately locate the lesions and plan the surgical paths,so as to improve the safety and efficiency of surgery.A 2D/3D multimodal medical image alignment algorithm was proposed,which firstly utilized the excellent feature extraction capability of Swin Transformer to construct an initial pose estimation model to realize the fast prediction of pose parameters.Then,in order to improve the robustness of the whole alignment method,a coarse alignment method based on the Grangeat relation was introduced.Finally,a fine alignment module based on gradient descent was designed.A fine alignment module based on gradient descent was designed to improve the accuracy of the whole alignment process,and the Sobel differential operator was combined with normalized correlation in this module to improve the sensitivity of the parameter optimization process.The experimental results show that the proposed alignment method meets the alignment requirements in the orthogonal and lateral alignment errors,and the alignment success rate is significantly improved.