在脑影像生成中构建潜空间因果模型，提升小样本下高质量反事实图像的生成能力。
Latent Causal Modeling for 3D Brain MRI Counterfactuals
- 在潜空间构建结构因果模型，结合VQ-VAE与闭式广义线性模型生成反事实图像。
- 在ADNI和NCANDA数据集上实现1mm高分辨率3D脑影像的高质量反事实生成。
- 适用于小样本脑部疾病研究，尤其适合阿尔茨海默病与青少年酒精神经发育分析。
结构化脑部磁共振成像（MRI）研究中的样本数量通常不足，难以充分训练深度学习模型。生成模型虽能有效学习数据分布并生成高质量MRI，但在训练数据分布之外仍难以生成多样且高质量的数据。使用为3D体数据设计的反事实因果模型可缓解此问题，但其在高维空间中准确建模因果关系存在挑战，常导致生成质量下降。为此，本文提出一种两阶段方法：首先利用VQ-VAE学习脑部MRI体积的紧凑嵌入；随后在该潜空间中集成因果模型，采用闭式广义线性模型（GLM）执行三步反事实推演。基于阿尔茨海默病神经影像计划（ADNI）和国家青少年酒精与神经发育研究联盟（NCANDA）提供的真实世界高分辨率（1 mm）MRI数据进行实验，结果表明该方法可有效生成高质量的3D脑部MRI反事实图像。
原文摘要 · Abstract (English)
The number of samples in structural brain MRI studies is often too small to properly train deep learning models. Generative models show promise in addressing this issue by effectively learning the data distribution and generating high-fidelity MRI. However, they struggle to produce diverse, high-quality data outside the distribution defined by the training data. One way to address this issue is to use causal models developed for 3D volume counterfactuals. However, accurately modeling causality in high-dimensional spaces is challenging, so these models generally generate 3D brain MRIs of lower quality. To address these challenges, we propose a two-stage method that constructs a Structural Causal Model (SCM) within the latent space. In the first stage, we employ a VQ-VAE to learn a compact embedding of the MRI volume. Subsequently, we integrate our causal model into this latent space and execute a three-step counterfactual procedure using a closed-form Generalized Linear Model (GLM). Our experiments conducted on real-world high-resolution MRI data (1 mm) provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) demonstrate that our method can generate high-quality 3D MRI counterfactuals.
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