Clinical decision-making relies heavily on causal reasoning and longitudinal analysis. For example, for a patient with Alzheimer's disease (AD), how will the brain grey matter atrophy in a year if intervened on the A-beta level in cerebrospinal fluid? The answer is fundamental to diagnosis and follow-up treatment. However, this kind of inquiry involves counterfactual medical images which can not be acquired by instrumental or correlation-based image synthesis models. Yet, such queries require counterfactual medical images, not obtainable through standard image synthesis models. Hence, a causal longitudinal image synthesis (CLIS) method, enabling the synthesis of such images, is highly valuable. However, building a CLIS model confronts three primary yet unmet challenges: mismatched dimensionality between high-dimensional images and low-dimensional tabular variables, inconsistent collection intervals of follow-up data, and inadequate causal modeling capability of existing causal graph methods for image data. In this paper, we established a tabular-visual causal graph (TVCG) for CLIS overcoming these challenges through a novel integration of generative imaging, continuous-time modeling, and structural causal models combined with a neural network. We train our CLIS based on the ADNI dataset and evaluate it on two other AD datasets, which illustrate the outstanding yet controllable quality of the synthesized images and the contributions of synthesized MRI to the characterization of AD progression, substantiating the reliability and utility in clinics.

翻译：临床决策在很大程度上依赖于因果推理与纵向分析。例如，对于阿尔茨海默病（AD）患者，若对脑脊液中的Aβ水平进行干预，一年后大脑灰质将如何萎缩？这一问题的答案对诊断与后续治疗至关重要。然而，此类研究涉及反事实医学图像，无法通过基于工具变量或相关性的图像合成模型获取。因此，能够合成此类图像的因果纵向图像合成（CLIS）方法具有重要价值。然而，构建CLIS模型面临三个主要且尚未解决的挑战：高维图像与低维表格变量之间的维度不匹配、随访数据采集间隔不一致，以及现有因果图方法对图像数据的因果建模能力不足。本文针对CLIS建立了一种表格-视觉因果图（TVCG），通过生成式成像、连续时间建模与结构因果模型结合神经网络的新颖集成方式克服了这些挑战。我们基于ADNI数据集训练CLIS模型，并在另外两个AD数据集上进行评估，结果表明合成图像具有优异且可控的质量，且合成MRI对AD进展表征具有贡献，证实了其在临床中的可靠性与实用性。