School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou 215006, China.
Comput Methods Programs Biomed. 2022 Sep;224:107025. doi: 10.1016/j.cmpb.2022.107025. Epub 2022 Jul 15.
Computer tomography (CT) to cone-beam computed tomography (CBCT) image registration plays an important role in radiotherapy treatment placement, dose verification, and anatomic changes monitoring during radiotherapy. However, fast and accurate CT-to-CBCT image registration is still very challenging due to the intensity differences, the poor image quality of CBCT images, and inconsistent structure information.
To address these problems, a novel unsupervised network named cross-domain fusion registration network (CDFRegNet) is proposed. First, a novel edge-guided attention module (EGAM) is designed, aiming at capturing edge information based on the gradient prior images and guiding the network to model the spatial correspondence between two image domains. Moreover, a novel cross-domain attention module (CDAM) is proposed to improve the network's ability to guide the network to effectively map and fuse the domain-specific features.
Extensive experiments on a real clinical dataset were carried out, and the experimental results verify that the proposed CDFRegNet can register CT to CBCT images effectively and obtain the best performance, while compared with other representative methods, with a mean DSC of 80.01±7.16%, a mean TRE of 2.27±0.62 mm, and a mean MHD of 1.50±0.32 mm. The ablation experiments also proved that our EGAM and CDAM can further improve the accuracy of the registration network and they can generalize well to other registration networks.
This paper proposed a novel CT-to-CBCT registration method based on EGAM and CDAM, which has the potential to improve the accuracy of multi-domain image registration.
计算机断层扫描（CT）到锥形束计算机断层扫描（CBCT）图像配准在放射治疗定位、剂量验证以及放射治疗期间监测解剖结构变化中起着重要作用。然而，由于强度差异、CBCT 图像质量差和结构信息不一致，快速准确的 CT 到 CBCT 图像配准仍然极具挑战性。
为了解决这些问题，提出了一种名为跨域融合配准网络（CDFRegNet）的新型无监督网络。首先，设计了一种新颖的边缘引导注意模块（EGAM），旨在基于梯度先验图像捕获边缘信息，并指导网络对两个图像域之间的空间对应关系进行建模。此外，提出了一种新颖的跨域注意模块（CDAM），以提高网络引导网络有效映射和融合特定域特征的能力。
在真实临床数据集上进行了广泛的实验，实验结果验证了所提出的 CDFRegNet 可以有效地将 CT 配准到 CBCT 图像，并获得最佳性能，与其他代表性方法相比，平均 DSC 为 80.01±7.16%，平均 TRE 为 2.27±0.62mm，平均 MHD 为 1.50±0.32mm。消融实验也证明了我们的 EGAM 和 CDAM 可以进一步提高配准网络的准确性，并且可以很好地推广到其他配准网络。
本文提出了一种基于 EGAM 和 CDAM 的新型 CT 到 CBCT 配准方法，有望提高多域图像配准的准确性。
