Li Tonglong, Chen Minheng, Li Mingying, Li Chuanyou, Kong Youyong
School of Computer Science and Engineering, Southeast University, Nanjing, China.
Department of Radiology, The 903rd Hospital of the People's Liberation Army, Hangzhou, Hangzhou, China.
Med Phys. 2025 Jul;52(7):e17896. doi: 10.1002/mp.17896. Epub 2025 May 21.
The registration of intraoperative x-ray images with preoperative CT images is an important step in image-guided surgery. However, existing regression-based methods lack an interpretable and stable mechanism when fusing information from intraoperative images and preoperative CT volumes. In addition, existing feature extraction and fusion methods limit the accuracy of pose regression.
The objective of this study is to develop a method that leverages both x-ray and computed tomography (CT) images to rapidly and robustly estimate an accurate initial registration within a broad search space. This approach integrates the strengths of learning-based registration with those of traditional registration methodologies, enabling the acquisition of registration outcomes across a wide search space at an accelerated pace.
We introduce a regression-based registration framework to address the aforementioned issues. We constrain the feature fusion process by training the network to reconstruct the high-dimensional feature representation vector of the preoperative CT volume in the embedding space from the input single-view x-ray, thereby enhancing the interpretability of feature extraction. Also, in order to promote the effective fusion and better extraction of local texture features and global information, we propose a lightweight cross-attention mechanism named lite cross-attention(LCAT). Besides, to meet the intraoperative requirements, we employ the intensity-based registration method CMA-ES to refine the result of pose regression.
Our approach is verified on both real and simulated x-ray data. Experimental results show that compared with the existing learning-based registration methods, the median rotation error of our method can reach 1.9 and the median translation error can reach 5.6 mm in the case of a large search range. When evaluated on 52 real x-ray images, we have a median rotation error of 1.6 and a median translation error of 3.8 mm due to the smaller search range. We also verify the role of the LCAT and embedding reconstruction modules in our registration framework. If they are not used, our registration performance will be reduced to approximately random initialization results.
During the experiments, our method demonstrates higher accuracy and larger capture range on both simulated images and real x-ray images compared to existing methods. The inspiring experimental results indicate the potential for future clinical application of our method.
术中X射线图像与术前CT图像的配准是图像引导手术中的重要步骤。然而，现有的基于回归的方法在融合术中图像和术前CT容积信息时缺乏可解释且稳定的机制。此外，现有的特征提取和融合方法限制了位姿回归的准确性。
本研究的目的是开发一种利用X射线和计算机断层扫描（CT）图像，在广泛的搜索空间内快速且稳健地估计准确初始配准的方法。这种方法将基于学习的配准优势与传统配准方法的优势相结合，能够在加速的情况下在广泛的搜索空间内获得配准结果。
我们引入了一个基于回归的配准框架来解决上述问题。我们通过训练网络从输入的单视图X射线重建术前CT容积在嵌入空间中的高维特征表示向量，从而约束特征融合过程，增强特征提取的可解释性。此外，为了促进局部纹理特征和全局信息的有效融合与更好提取，我们提出了一种名为轻量级交叉注意力（LCAT）的轻量级交叉注意力机制。此外，为满足术中需求，我们采用基于强度的配准方法CMA - ES来细化位姿回归结果。
我们的方法在真实和模拟X射线数据上均得到验证。实验结果表明，与现有的基于学习的配准方法相比，在大搜索范围的情况下，我们方法的旋转误差中位数可达1.9°，平移误差中位数可达5.6毫米。在52张真实X射线图像上进行评估时，由于搜索范围较小，我们的旋转误差中位数为1.6°，平移误差中位数为3.8毫米。我们还验证了LCAT和嵌入重建模块在我们配准框架中的作用。如果不使用它们，我们的配准性能将降低到近似随机初始化的结果。
在实验过程中，与现有方法相比，我们的方法在模拟图像和真实X射线图像上均表现出更高的准确性和更大的捕获范围。令人鼓舞的实验结果表明了我们方法未来临床应用的潜力。
