PDF(Pubmed)
Abstract：
UNASSIGNED: Studying the spatiotemporal patterns of amyloid accumulation in the brain over time is crucial in understanding Alzheimer\'s disease (AD). Positron Emission Tomography (PET) imaging plays a pivotal role because it allows for the visualization and quantification of abnormal amyloid beta (Aβ) load in the living brain, providing a powerful tool for tracking disease progression and evaluating the efficacy of anti-amyloid therapies. Generative artificial intelligence (AI) can learn complex data distributions and generate realistic synthetic images. In this study, we demonstrate for the first time the potential of Generative Adversarial Networks (GANs) to build a low-dimensional representation space that effectively describes brain amyloid load and its dynamics.
UNASSIGNED: Using a cohort of 1,259 subjects with AV45 PET images from the Alzheimer\'s Disease Neuroimaging Initiative (ADNI), we develop a 3D GAN model to project images into a latent representation space and generate back synthetic images. Then, we build a progression model on the representation space based on non-parametric ordinary differential equations to study brain amyloid evolution.
UNASSIGNED: We found that global SUVR can be accurately predicted with a linear regression model only from the latent representation space (RMSE = 0.08 ± 0.01). We generated synthetic PET trajectories and illustrated predicted Aβ change in four years compared with actual progression.
UNASSIGNED: Generative AI can generate rich representations for statistical prediction and progression modeling and simulate evolution in synthetic patients, providing an invaluable tool for understanding AD, assisting in diagnosis, and designing clinical trials. The aim of this study was to illustrate the huge potential that generative AI has in brain amyloid imaging and to encourage its advancement by providing use cases and ideas for future research tracks.
UNASSIGNED: Using a cohort of 1,259 subjects with AV45 PET images from the Alzheimer\'s Disease Neuroimaging Initiative (ADNI), we develop a 3D GAN model to project images into a latent representation space and generate back synthetic images. Then, we build a progression model on the representation space based on non-parametric ordinary differential equations to study brain amyloid evolution.
UNASSIGNED: We found that global SUVR can be accurately predicted with a linear regression model only from the latent representation space (RMSE = 0.08 ± 0.01). We generated synthetic PET trajectories and illustrated predicted Aβ change in four years compared with actual progression.
UNASSIGNED: Generative AI can generate rich representations for statistical prediction and progression modeling and simulate evolution in synthetic patients, providing an invaluable tool for understanding AD, assisting in diagnosis, and designing clinical trials. The aim of this study was to illustrate the huge potential that generative AI has in brain amyloid imaging and to encourage its advancement by providing use cases and ideas for future research tracks.
摘要：
■随着时间的推移，研究大脑中淀粉样蛋白积累的时空模式对于理解阿尔茨海默病（AD）至关重要。正电子发射断层扫描（PET）成像起着关键作用，因为它可以对活体大脑中异常的淀粉样β（Aβ）负荷进行可视化和量化，为跟踪疾病进展和评估抗淀粉样蛋白疗法的疗效提供了强大的工具。生成人工智能（AI）可以学习复杂的数据分布并生成逼真的合成图像。在这项研究中,我们首次展示了生成对抗网络(GAN)构建低维表示空间的潜力,该空间有效地描述了脑淀粉样蛋白负荷及其动力学.
■使用一组1,259名受试者的AV45PET图像来自阿尔茨海默病神经影像学计划（ADNI），我们开发了一个3DGAN模型，将图像投影到潜在的表示空间，并生成合成图像。然后，我们基于非参数常微分方程在表示空间上建立进展模型来研究大脑淀粉样蛋白的进化。
■我们发现，仅从潜在表示空间（RMSE=0.08±0.01），就可以用线性回归模型准确预测全局SUVR。我们生成了合成PET轨迹，并说明了与实际进展相比，四年内预测的Aβ变化。
■生成AI可以为统计预测和进展建模生成丰富的表示，并模拟合成患者的进化，为理解AD提供了宝贵的工具，协助诊断,设计临床试验。这项研究的目的是说明生成人工智能在脑淀粉样蛋白成像中的巨大潜力，并通过为未来的研究轨迹提供用例和想法来鼓励其进步。
■使用一组1,259名受试者的AV45PET图像来自阿尔茨海默病神经影像学计划（ADNI），我们开发了一个3DGAN模型，将图像投影到潜在的表示空间，并生成合成图像。然后，我们基于非参数常微分方程在表示空间上建立进展模型来研究大脑淀粉样蛋白的进化。
■我们发现，仅从潜在表示空间（RMSE=0.08±0.01），就可以用线性回归模型准确预测全局SUVR。我们生成了合成PET轨迹，并说明了与实际进展相比，四年内预测的Aβ变化。
■生成AI可以为统计预测和进展建模生成丰富的表示，并模拟合成患者的进化，为理解AD提供了宝贵的工具，协助诊断,设计临床试验。这项研究的目的是说明生成人工智能在脑淀粉样蛋白成像中的巨大潜力，并通过为未来的研究轨迹提供用例和想法来鼓励其进步。
