<p>Deformable medical image registration is an essential process requiring characteristics to be extracted and aligned from two images to provide exact correspondence, a necessity for accurate registration. The Transformer can improve predictive capacities, as demonstrated by recent experiments. Nevertheless, there are significant obstacles when directly applying it to big databases like OASIS. These include significant memory needs, quadratic temporal complexity, and intrinsic limitations in the encoder-decoder architecture. Even with the development of advanced registration models, achieving precise and effective deformable registration is still difficult, particularly in situations with significant volumetric deformations. We use a Selective Transformer (ST) and Hierarchical Reciprocal Attention (HRA) to address these challenges. To minimize computing complexity and optimize resource allocation for more effective processing, ST assists in calculating the diversity of voxels and chooses those with a broad range of diversity. Using an encoder-decoder architecture, HRA uses high-level features to link layers, allowing information to flow from a high to a lower level and vice versa. We use Reciprocal Attention (RA) instead of skip connections to facilitate the flow of information between the feature extractor and the feature reconstructor. This method maximizes the model’s capacity to accurately capture and anticipate deformations by thoroughly integrating complex spatial data and abstract representations. We utilized two well-known pre-aligned brain MRI datasets, OASIS and LPBA40, to benchmark our model against other established registration techniques. Our evaluations frequently demonstrate that our network surpasses state-of-the-art methods across various metrics, including Dice score and Jacobian.</p>