Understanding and predicting the progression of neurodegenerative diseases remains a major challenge in medical AI, with significant implications for early diagnosis, disease monitoring, and treatment planning. However, most available longitudinal neuroimaging datasets are temporally sparse with a few follow-up scans per subject. This scarcity of temporal data limits our ability to model and accurately capture the continuous anatomical changes related to disease progression in individual subjects. To address this problem, we propose a novel 4D (3DxT) diffusion-based generative framework that effectively models and synthesizes longitudinal brain anatomy over time, conditioned on available clinical variables such as health status, age, sex, and other relevant factors. Moreover, while most current approaches focus on manipulating image intensity or texture, our method explicitly learns the data distribution of topology-preserving spatiotemporal deformations to effectively capture the geometric changes of brain structures over time. This design enables the realistic generation of future anatomical states and the reconstruction of anatomically consistent disease trajectories, providing a more faithful representation of longitudinal brain changes. We validate our model through both synthetic sequence generation and downstream longitudinal disease classification, as well as brain segmentation. Experiments on two large-scale longitudinal neuroimage datasets demonstrate that our method outperforms state-of-the-art baselines in generating anatomically accurate, temporally consistent, and clinically meaningful brain trajectories. Our code is available on Github.

翻译：理解和预测神经退行性疾病的进展仍是医学人工智能领域的重大挑战，这对早期诊断、疾病监测和治疗规划具有重要影响。然而，现有的大多数纵向神经影像数据集在时间维度上较为稀疏，每个受试者仅有少数随访扫描。这种时间数据的稀缺性限制了我们建模并准确捕捉个体受试者疾病进展相关的连续解剖变化的能力。为解决这一问题，我们提出了一种新型四维扩散生成框架，该框架以临床变量（如健康状况、年龄、性别及其他相关因素）为条件，有效建模并合成随时间变化的纵向脑解剖结构。此外，当前多数方法聚焦于操纵图像强度或纹理，而我们的方法则显式学习拓扑保持的时空形变的数据分布，以有效捕捉脑结构随时间推移的几何变化。这一设计使得未来解剖状态的逼真生成和解剖一致疾病轨迹的重建成为可能，从而更真实地呈现纵向脑部变化。我们通过合成序列生成、下游纵向疾病分类及脑部分割任务验证了模型的有效性。在两个大规模纵向神经影像数据集上的实验表明，我们的方法在生成解剖精确、时间一致且具有临床意义的脑部轨迹方面优于现有最优基线方法。我们的代码已发布于Github。