用形变场增强扩散模型，让缺失脑部MRI更可信、更灵活。
Trustworthy Longitudinal Brain MRI Completion: A Deformation-Based Approach with KAN-Enhanced Diffusion Model
- 基于形变场引导的扩散模型，提升生成图像真实性
- 在OASIS-3数据集上PSNR提升5.6%，SSIM提升0.12
- 支持多种模态和属性图，适用性广
纵向脑部MRI对生命全程研究至关重要，但高流失率常导致数据缺失，影响分析。现有深度生成模型多仅依赖图像强度，存在两大缺陷：一是生成图像保真度不足，下游研究可靠性存疑；二是引导方式固定，限制应用场景灵活性。为此，本文提出DF-DiffCom——一种融合柯尔莫戈洛夫-阿诺德网络（KAN）的扩散模型，通过智能利用形变场实现可信的纵向脑影像补全。在OASIS-3数据集上，该方法优于当前最优模型，PSNR提升5.6%，SSIM提升0.12。更重要的是，其模态无关特性可无缝扩展至多种MRI模态，甚至脑组织分割等属性图。
原文摘要 · Abstract (English)
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.
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