## 1 Introduction
More and more studies have begun to explore how to use unsupervised methods to improve registration performance, such as the work by [PERSON] et al. [14]. A key challenge in unsupervised learning for registration networks has been determining the appropriate loss function in the absence of supervised information. With the introduction of the space transformation network (STN) [15], this problem was perfectly solved. As a differentiable module, STN can be arbitrarily inserted into any position of the neural network, and it can carry out gradient backpropagation along with the network as a whole. The insertion of the STN made it possible to warp the moving image to generate warped images during the training process, perform similarity calculations [16] by applying STN to the field of medical images for the first time, and perform end-to-end registration of 2D cardiac cine MR scans, and it achieved comparable accuracy to traditional deformable registration methods and greatly shortened registration time. Later, [PERSON] et al. [17] proposed VoxelMorph, which is a deformable registration model based on an unsupervised full convolutional neural network. The model is trained by punishing the appearance difference between images and the spatial gradient of the deformation field. This model also achieves the same results as the most advanced traditional registration algorithm. [PERSON] et al. [18] proposed a lightweight unsupervised registration model, FAIM, and strengthened the penalty for irreversible deformation. Later, [PERSON] et al. [19] proposed a deep-learning framework VTN for unsupervised affine and deformable registration and used it for 3D cardiac cine MRI and 4D chest CT registration. The results showed that although the smoothness of deformation is a problem, the registration performance of this method exceeds that of traditional algorithms, such as Elastix [20], and has achieved remarkable results in other aspects. In addition, [PERSON] et al. [21] introduced TransMorph, a hybrid Transformer ConvNet model for volume medical image registration. Accurate patient-to-patient registration is extremely challenging due to the variability in brain structures across different individuals. In addition, voxel folding in the deformation field can significantly affect image topology and deformation authenticity. Voxel folding refers to the overlapping of voxels during deformation, leading to inaccuracies. In order to solve the above problems, the traditional registration algorithm can effectively deal with the problems of voxel folding and deformation smoothness based on mathematical models and optimization methods, especially in maintaining the local structure and overall consistency, but it has a slow registration speed in large-scale data or real-time applications, and low registration accuracy when dealing with complex deformation or nonlinear transformation. Deep-learning-based registration algorithms can achieve faster and more accurate registration by learning transformation patterns from large datasets. These algorithms perform better than traditional methods, especially when dealing with complex nonlinear deformations. However, they may still struggle with voxel folding and maintaining deformation smoothness. To enhance registration speed and accuracy, while also addressing voxel folding and deformation smoothness, we propose the multi-constraint cascaded attention network (MCANet). The network architecture consists of two identical subnets, one of which is the extended self-attention network (DSNet). This paper introduces an attention mechanism based on attention gates to the convolutional neural network. This improves the network's sensitivity to regions with large differences between patient images by inhibiting feature activation in the alignment region. As a result, the network better adapts to inter-patient image variations, enhancing registration accuracy.