Abstract
Characterizing a preclinical stage of Alzheimer's Disease (AD) via single imaging is difficult as its early symptoms are quite subtle. Therefore, many neuroimaging studies are curated with various imaging modalities, e.g., MRI and PET, however, it is often challenging to acquire all of them from all subjects and missing data become inevitable. In this regards, in this paper, we propose a framework that generates unobserved imaging measures for specific subjects using their existing measures, thereby reducing the need for additional examinations. Our framework transfers modality-specific style while preserving AD-specific content. This is done by domain adversarial training that preserves modality-agnostic but AD-specific information, while a generative adversarial network adds an indistinguishable modality-specific style. Our proposed framework is evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) study and compared with other imputation methods in terms of generated data quality. Small average Cohen's $d$ $< 0.19$ between our generated measures and real ones suggests that the synthetic data are practically usable regardless of their modality type.
Abstract (translated)
通过单一影像手段来表征阿尔茨海默病（AD）的临床前期阶段是困难的，因为其早期症状非常细微。因此，许多神经影像学研究通常会结合使用多种成像模式（如MRI和PET），然而，从所有受试者中获取所有这些模态的数据往往具有挑战性，并且不可避免地会出现数据缺失的情况。为此，在本文中，我们提出了一种框架，该框架能够根据现有测量结果生成特定受试者的未观察到的影像指标，从而减少对额外检查的需求。我们的框架在转移特异性模式风格的同时保持了AD特有的内容信息。这通过领域对抗训练实现，保留模态无关但与AD相关的特定信息，而生成对抗网络则添加不可区分的模态特定风格。 我们在阿尔茨海默病神经影像学倡议（ADNI）研究中评估了我们提出的框架，并将合成数据的质量与其他插补方法进行了比较。我们的生成指标和实际指标之间的平均Cohen's d值小于0.19，这表明无论模态类型如何，合成数据在实践中都是可以使用的。 简而言之： 本文提出了一种利用现有影像数据生成缺失影像测量的方法框架，该框架能够保留阿尔茨海默病特有的信息，并添加不可区分的模态特有风格。通过对ADNI研究中的评估及与其他插补方法的比较，证实了所生成的数据在实际应用中具有可行性与实用性。
URL
https://arxiv.org/abs/2503.02898
