Deformable medical image registration is an important component of medical image analysis. Transformer networks have recently received more attention due to their ability to improve image registration by utilizing long-range spatial relationships. However, these approaches often struggle to accurately align images compromised by artifacts, typically caused by patients’ organ movement during scans across various diagnostic devices. To solve this challenge, this study introduces a triple-stream architecture that enhances organ alignment accuracy while maintaining the anatomical structure. Our proposed approach incorporates a cascade-based cross-attention technique within a Tri-UNet framework, allowing for the continuous integration of moving, fixed, and new moving images. The technique addresses the optimal alignment and extensive motion artifacts often encountered in clinical imaging. To ensure that the anatomical structure remains maintained during deformations, we optimized the framework by integrating motion correction techniques that generated the final deformation ϕ (DVF) by the maintaining anatomical structure from the multi-scale deformation of two primary sources: moving-fixed and new moving-fixed. This technique significantly aligns image pairs and reduces motion artifacts from deformation fields, resulting in stable and accurate deformation. The results show that the proposed methods achieve state-of-the-art performance compared with other learning-based methods. Our proposed method achieved an average Dice similarity coefficient of 0.787 and 0.726 on Open Access Series of Imaging Studies (OASIS) and LONI Probabilistic Brain Atlas (LPBA40), respectively.