用因果图指导生成3D脑部MRI反事实图像，评估其真实性与可逆性。
Evaluation of 3D Counterfactual Brain MRI Generation
- 基于因果图和解剖结构约束，以区域体积为条件生成反事实图像。
- 目标区域修改有效，但非目标区域结构保持不佳。
- 适合研究阿尔茨海默病机制或需可解释生成模型的医学影像学者。
反事实生成为模拟医学影像中假设性变化提供了原则性框架，可用于理解疾病机制并生成生理上合理的数据。然而，由于数据稀缺、结构复杂以及缺乏标准化评估协议，生成符合解剖与因果约束的真实3D脑部T1加权磁共振图像（T1w MRIs）仍具挑战。本文将六种生成模型改造为3D反事实方法，引入基于因果图的解剖引导框架，以区域脑体积作为直接条件输入。在阿尔茨海默病神经影像计划（ADNI）的T1w MRIs上，评估各模型在组合性、可逆性、真实性、有效性及最小性方面的表现；同时在国家青少年酒精与神经发育研究联盟（NCANDA）的数据上测试泛化能力。结果表明，解剖引导条件能有效修改目标区域，但在保持非目标结构方面存在局限。该基准为更可解释、临床相关的脑部MRI生成建模奠定基础，也凸显了需设计更准确捕捉解剖相互依赖关系的新架构。
原文摘要 · Abstract (English)
Counterfactual generation offers a principled framework for simulating hypothetical changes in medical imaging, with potential applications in understanding disease mechanisms and generating physiologically plausible data. However, generating realistic structural 3D brain MRIs that respect anatomical and causal constraints remains challenging due to data scarcity, structural complexity, and the lack of standardized evaluation protocols. In this work, we convert six generative models into 3D counterfactual approaches by incorporating an anatomy-guided framework based on a causal graph, in which regional brain volumes serve as direct conditioning inputs. Each model is evaluated with respect to composition, reversibility, realism, effectiveness and minimality on T1-weighted brain MRIs (T1w MRIs) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). In addition, we test the generalizability of each model with respect to T1w MRIs of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA). Our results indicate that anatomically grounded conditioning successfully modifies the targeted anatomical regions; however, it exhibits limitations in preserving non-targeted structures. Beyond laying the groundwork for more interpretable and clinically relevant generative modeling of brain MRIs, this benchmark highlights the need for novel architectures that more accurately capture anatomical interdependencies.
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