Longitudinal brain MRI is essential for lifespan study, yet high attrition rates often lead to missing data, complicating analysis. Deep generative models have been explored, but most rely solely on image intensity, leading to two key limitations: 1) the fidelity or trustworthiness of the generated brain images are limited, making downstream studies questionable; 2) the usage flexibility is restricted due to fixed guidance rooted in the model structure, restricting full ability to versatile application scenarios. To address these challenges, we introduce DF-DiffCom, a Kolmogorov-Arnold Networks (KAN)-enhanced diffusion model that smartly leverages deformation fields for trustworthy longitudinal brain image completion. Trained on OASIS-3, DF-DiffCom outperforms state-of-the-art methods, improving PSNR by 5.6% and SSIM by 0.12. More importantly, its modality-agnostic nature allows smooth extension to varied MRI modalities, even to attribute maps such as brain tissue segmentation results.

翻译：纵向脑部MRI对于生命历程研究至关重要，然而高失访率常导致数据缺失，使分析复杂化。深度生成模型已被探索用于此任务，但多数仅依赖图像强度，导致两个关键局限：1）生成脑部图像的保真度或可靠性有限，使得下游研究结果存疑；2）由于模型结构中固化的引导机制，使用灵活性受限，难以充分发挥其在多样化应用场景中的潜力。为解决这些挑战，我们提出了DF-DiffCom——一种基于Kolmogorov-Arnold Networks（KAN）增强的扩散模型，其通过巧妙利用变形场实现可靠的纵向脑部图像补全。在OASIS-3数据集上的训练表明，DF-DiffCom在性能上超越现有最优方法，将PSNR提升5.6%，SSIM提高0.12。更重要的是，其模态无关特性使其能平滑扩展至多种MRI模态，甚至可应用于脑组织分割结果等属性图谱。