<p>Among men globally, prostate cancer ranks as one of the most frequently occurring types of cancer. Timely identification and accurate treatment rely on high-quality medical image registration, especially the precise registration of multimodal images such as diffusion-weighted imaging (DWI) and T2-weighted MRI. However, due to the low resolution, high noise and blurred boundaries of DWI, the registration with T2-weighted images is extremely challenging. The significant differences between the two imaging modalities make the multimodal registration task complex, which directly affects the accuracy of diagnosis and treatment. To address these problems, this paper proposes a dense convolutional and gated feature extraction network (DG-Morph), which leverages the strengths of Convolutional Neural Networks (CNN) and Transformer models to enhance the efficiency of feature representation and extraction. The gated residual fusion module (GRFM) dynamically fuses features, the Multi-scale Transformer module (MST) improves multi-scale feature extraction, and the dense 3D-convolutional(Dense Conv3D) block optimizes feature reuse. Experimental results show that DG-Morph significantly outperforms traditional methods regarding the Dice Similarity Coefficient(DSC) and deformation field smoothness, showing excellent performance in complex multimodal medical image registration. These results show that DG-Morph has significant advantages in accuracy and robustness in multimodal registration, especially for complex multimodal medical image registration tasks.</p>