用单次MRI生成随时间变化的脑部影像，解决数据缺失问题。
Conditional Diffusion Model for Longitudinal Medical Image Generation
- 基于扩散模型，输入单次MRI和时间编码生成连续影像。
- 生成图像质量优于现有方法，能有效模拟疾病进展过程。
- 适合阿尔茨海默病研究与临床随访数据补全场景。
阿尔茨海默病进展缓慢，涉及多种生物因素的复杂交互。纵向医学影像数据可捕捉这一动态过程，但常因患者脱落、随访时间不规律及观测周期长短不一导致数据缺失。为此，我们设计了一种基于扩散模型的3D纵向医学影像生成方法，仅需一次磁共振成像（MRI）作为条件输入，并结合时间-访问编码，实现对源图像与目标图像间变化的有效控制。实验结果表明，该方法生成的图像质量优于其他对比方法。
原文摘要 · Abstract (English)
Alzheimers disease progresses slowly and involves complex interaction between various biological factors. Longitudinal medical imaging data can capture this progression over time. However, longitudinal data frequently encounter issues such as missing data due to patient dropouts, irregular follow-up intervals, and varying lengths of observation periods. To address these issues, we designed a diffusion-based model for 3D longitudinal medical imaging generation using single magnetic resonance imaging (MRI). This involves the injection of a conditioning MRI and time-visit encoding to the model, enabling control in change between source and target images. The experimental results indicate that the proposed method generates higher-quality images compared to other competing methods.
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