Zhu Jianjun, Li Heng, Ai Danni, Yang Qi, Fan Jingfan, Huang Yong, Song Hong, Han Yechen, Yang Jian
School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
Comput Methods Programs Biomed. 2021 Feb;199:105901. doi: 10.1016/j.cmpb.2020.105901. Epub 2020 Dec 22.
Background and objective Fusion of the preoperative computed tomography angiography and intraoperative X-ray angiography images can considerably enhance the visual perception of physicians during percutaneous coronary interventions. This technique can provide 3D information of the arteries and reduce the uncertainty of 2D guidance images. For this purpose, 3D/2D vascular registration with high accuracy and robustness is crucial for performing accurate surgery. Methods In this study, we propose an iterative closest graph matching (ICGM) method that utilizes an alternative iteration framework including correspondence and transformation phases. A coarse-to-fine matching approach based on redundant graph matching is proposed for the correspondence phase. The transformation phase involves rigid and non-rigid transformations, in which rigid transformation is calculated using a closed-form solution, and non-rigid transformation is achieved using a statistical shape model established from a synthetic deformation dataset. Results The proposed method is evaluated and compared with nine state-of-the-art methods on simulated data and clinical datasets. Experiments demonstrate that our method is insensitive to the pose of data and robust to noise and deformation. Moreover, it outperforms other methods in terms of registering real data. Conclusions Given its high capture range, the proposed method can register 3D vessels without prior initialization in clinical practice.
背景与目的 术前计算机断层血管造影（CTA）图像与术中X射线血管造影图像的融合能够显著增强医生在经皮冠状动脉介入治疗过程中的视觉感知。该技术可提供动脉的三维信息，并减少二维引导图像的不确定性。为此，高精度且稳健的三维/二维血管配准对于实施精准手术至关重要。方法 在本研究中，我们提出一种迭代最近邻图匹配（ICGM）方法，该方法利用了一个包括对应阶段和变换阶段的交替迭代框架。在对应阶段，提出了一种基于冗余图匹配的由粗到精的匹配方法。变换阶段涉及刚性变换和非刚性变换，其中刚性变换使用闭式解计算，非刚性变换通过从合成变形数据集建立的统计形状模型实现。结果 在模拟数据和临床数据集上对所提出的方法进行了评估，并与九种先进方法进行了比较。实验表明，我们的方法对数据姿态不敏感，对噪声和变形具有鲁棒性。此外，在配准真实数据方面，它优于其他方法。结论 鉴于其高捕获范围，所提出的方法在临床实践中无需事先初始化即可对三维血管进行配准。
