Abstract: Abstract: With the widespread application of deep learning in medical image analysis， significant progress has been made in medical image registration methods. However， existing approaches still have limitations in feature extraction and fusion， particularly in handling independent anatomical structure information between image pairs. To address this issue， this paper proposes an efficient cross-attention-based medical image registration network. The network employs two parallel branches and independently processes and fuses image features through the cross-attention mechanism， effectively improving registration accuracy. To reduce computational complexity and memory consumption， this paper introduces an efficient cross-attention mechanism that preserves global feature capturing capability while enhancing computational efficiency. Additionally， the proposed model combines Transformer and Convolutional Neural Networks （CNN）， utilizing the Transformer to capture long-range dependencies and the CNN to extract local features. This approach reduces the model’s parameter count and improves training efficiency. To evaluate the performance of the proposed model， experiments were conducted on the OASIS and BraTs2018 datasets. The proposed model achieves Dice coefficients of 0.804 and 0.732 on these two datasets， respectively， demonstrating superior registration performance compared to other methods. These experimental results indicate that the proposed model not only improves the accuracy of medical image registration but also optimizes computational efficiency， making it highly applicable in various scenarios.

Key words: Key words: medical image processing, image registration, Transformer model, cross-attention