In clinical practice, tri-modal medical image fusion, compared to the existing dual-modal technique, can provide a more comprehensive view of the lesions, aiding physicians in evaluating the disease's shape, location, and biological activity. However, due to the limitations of imaging equipment and considerations for patient safety, the quality of medical images is usually limited, leading to sub-optimal fusion performance, and affecting the depth of image analysis by the physician. Thus, there is an urgent need for a technology that can both enhance image resolution and integrate multi-modal information. Although current image processing methods can effectively address image fusion and super-resolution individually, solving both problems synchronously remains extremely challenging. In this paper, we propose TFS-Diff, a simultaneously realize tri-modal medical image fusion and super-resolution model. Specially, TFS-Diff is based on the diffusion model generation of a random iterative denoising process. We also develop a simple objective function and the proposed fusion super-resolution loss, effectively evaluates the uncertainty in the fusion and ensures the stability of the optimization process. And the channel attention module is proposed to effectively integrate key information from different modalities for clinical diagnosis, avoiding information loss caused by multiple image processing. Extensive experiments on public Harvard datasets show that TFS-Diff significantly surpass the existing state-of-the-art methods in both quantitative and visual evaluations. The source code will be available at GitHub.

翻译：在临床实践中，相较于现有双模态技术，三模态医学图像融合能够提供更全面的病灶视图，助力医师评估疾病的形态、位置及生物活性。然而，受限于成像设备性能及患者安全考量，医学图像质量通常受限，导致融合性能欠佳，并影响医师对图像的深度分析。因此，迫切需要一种既能增强图像分辨率又能整合多模态信息的技术。尽管当前图像处理方法可分别有效解决图像融合与超分辨率问题，但同步解决这两个问题仍极具挑战性。本文提出TFS-Diff模型，可同时实现三模态医学图像融合与超分辨率重建。具体而言，TFS-Diff基于扩散模型的随机迭代去噪过程生成机制。我们同时设计了简洁的目标函数与所提出的融合超分辨率损失函数，有效评估融合过程中的不确定性并确保优化过程的稳定性。此外，通过引入通道注意力模块，可高效整合不同模态的关键临床诊断信息，避免多次图像处理造成的信息损失。在公开哈佛数据集上的大量实验表明，TFS-Diff在定量评估与视觉评价上均显著超越现有最优方法。相关源代码将在GitHub开源。