可变形配准在多项医学影像分析任务中具有重要作用．然而，卷积神经网络的固定感受野难以充分捕捉大脑的空间上下文信息，Transformer架构能有效捕获全局信息但计算开销大．因此，本文提出一种基于多尺度膨胀残差卷积和双重注意力的可变形配准网络．该网络采用多尺度膨胀残差卷积同时捕获局部细节和广泛的上下文信息，通过双重注意力模块增强特征的表达能力，并在解码器部分引入动态上采样精细重建高频细节以确保变形场的拓扑完整性．在两个公共脑部数据集上的实验结果表明，本文所提模型在戴斯相似性系数（DSC）、95%豪斯多夫距离（HD95）和非正雅可比行列式百分比等多个指标上实现了较高的配准效果，表明其具有更强的配准精度和更平滑的变形场．

Deformable registration plays a significant role in multiple medical image analysis tasks. However, the fixed receptive field of convolutional neural networks is difficult to fully capture the spatial context information of the brain, while the Transformer architecture can effectively capture global information but has a high computational cost. Therefore, this paper proposes a deformable registration network based on multi-scale dilated residual convolution and dual attention. This network adopts a multi-scale dilated residual convolution to simultaneously capture local details and extensive context information. The dual attention module is adopted to enhance the expressive ability of features. The decoder section introduces dynamic upsampling to finely reconstruct high-frequency details to ensure the topological integrity of the deformation field. Experiments were conducted on two public brain datasets. The results demonstrate that the proposed model achieves high registration effects in multiple indicators such as Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95) and percentage of non-positive Jacobian determinant, demonstrating superior registration precision and smoother deformation fields.