“An innovative study has achieved significant results in addressing the issues of blurry texture details and low contrast in multimodal medical image fusion. The research team has proposed a multimodal medical image fusion method using a structural functional cross neural network, which has brought new breakthroughs to the field of medical image analysis. This method first designs a structural functional cross functional neural network model, which effectively extracts the structural and functional information of medical images, realizes the interaction between the two types of information, and can accurately capture the texture details of images. Secondly, the research team has developed a novel attention mechanism that significantly improves the contrast and contour clarity of fused images by dynamically adjusting the weights of structural and functional information. Finally, they also designed a decomposition process from the fused image to the source image, ensuring that the fused image contains richer detail information. Compared with the seven high-level methods proposed in recent years, this method has achieved an average improvement of 22.87%, 19.64%, 23.02%, 12.70%, 6.79%, and 30.35% in objective evaluation indicators such as AG, EN, SF, MI, QAB/F, and CC. This achievement not only demonstrates the advantages of this method in texture details and contrast, but also proves its superiority in subjective visual and objective indicators. The success of this research has brought new perspectives and solutions to the field of medical image analysis, which is expected to provide doctors with more accurate and comprehensive diagnostic basis, and help the medical field achieve more accurate diagnosis and treatment.”