arXiv 2508.028802025-08-22eess.IVcs.CV

3D反事实脑MRI生成的评估

Evaluation of 3D Counterfactual Brain MRI Generation

Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA Department of Radiology, Stanford University, Stanford, CA, USA Dept. of Psychiatry \& Behavioral Sciences, Stanford University, Stanford, CA, USA Department of Computer Science, Stanford University, Stanford, CA, USA Department of Bioengineering, Stanford University, Stanford, CA, USA Department of Electrical Engineering, Stanford University, Stanford, CA, USA

Pengwei Sun, Wei Peng, Lun Yu Li, Yixin Wang, Kilian M. Pohl

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AI总结：

本文通过解剖学引导的因果图框架将六种生成模型转化为3D反事实脑MRI方法，并在ADNI和NCANDA数据集上从组合性、可逆性、真实感、有效性和最小性五个维度进行评估，发现现有模型能修改目标区域但难以保留非目标结构，揭示了新型架构的必要性。

摘要：

反事实生成为模拟医学影像中的假设性变化提供了一个原则性框架，在理解疾病机制和生成生理上合理的数据方面具有潜在应用。然而，生成既尊重解剖学和因果约束又具有真实感的结构性3D脑MRI仍然具有挑战性，原因在于数据稀缺、结构复杂性以及缺乏标准化的评估协议。在这项工作中，我们通过引入一个基于因果图的解剖学引导框架，将六种生成模型转化为3D反事实方法，其中区域脑体积作为直接条件输入。每个模型都在来自阿尔茨海默病神经影像学倡议（ADNI）的T1加权脑MRI（T1w MRI）上，从组合性、可逆性、真实感、有效性和最小性五个方面进行评估。此外，我们还测试了每个模型对青少年国家酒精与神经发育联盟（NCANDA）T1w MRI的泛化能力。我们的结果表明，基于解剖学的条件设定能够成功修改目标解剖区域；然而，它在保留非目标结构方面表现出局限性。除了为更具可解释性和临床相关性的脑MRI生成建模奠定基础外，该基准还突显了对能够更准确捕捉解剖学相互依赖关系的新型架构的需求。

英文摘要：

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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