标题： MRExtrap：使用潜在空间中的线性建模进行脑部MRI的纵向老化分析 显示英文标题

标题： MRExtrap: Longitudinal Aging of Brain MRIs using Linear Modeling in Latent Space

摘要： 模拟3D脑部MRI扫描中的衰老过程可以揭示神经疾病（如阿尔茨海默病）中的疾病进展模式。当前基于深度学习的生成模型通常通过从单个观察到的扫描中预测未来的扫描来解决这个问题。我们研究了通过卷积自编码器（MRExtrap）潜在空间中的线性模型来建模大脑衰老。我们的方法MRExtrap基于我们的观察，即在脑部MRI上训练的自编码器创建的潜在空间中，衰老轨迹似乎大致呈线性。我们对脑部MRI进行自编码器训练以创建潜在空间，并研究这些潜在空间如何通过基于年龄的线性外推来预测未来的MRI，使用估计的潜在进展率$\boldsymbol{\beta}$。对于单次扫描预测，我们提出使用群体平均和个体特定的线性进展率先验。我们还证明，在存在额外扫描的情况下，可以使用贝叶斯后验采样灵活地更新预测，提供一种用于个体特定优化的机制。在ADNI数据集上，MRExtrap准确预测了衰老模式，并在单体积脑部衰老预测方面超越了基于GAN的基线。我们还展示了多扫描条件的应用，以整合个体特定的进展率。最后，我们表明MRExtrap线性框架中的潜在进展率与之前研究的结构萎缩率中的疾病和年龄相关的衰老模式相关。MRExtrap为基于年龄的3D脑部MRI生成提供了一种简单且稳健的方法，尤其在有多个纵向观察的场景中非常有价值。 显示英文摘要

摘要： Simulating aging in 3D brain MRI scans can reveal disease progression patterns in neurological disorders such as Alzheimer's disease. Current deep learning-based generative models typically approach this problem by predicting future scans from a single observed scan. We investigate modeling brain aging via linear models in the latent space of convolutional autoencoders (MRExtrap). Our approach, MRExtrap, is based on our observation that autoencoders trained on brain MRIs create latent spaces where aging trajectories appear approximately linear. We train autoencoders on brain MRIs to create latent spaces, and investigate how these latent spaces allow predicting future MRIs through linear extrapolation based on age, using an estimated latent progression rate $\boldsymbol{\beta}$. For single-scan prediction, we propose using population-averaged and subject-specific priors on linear progression rates. We also demonstrate that predictions in the presence of additional scans can be flexibly updated using Bayesian posterior sampling, providing a mechanism for subject-specific refinement. On the ADNI dataset, MRExtrap predicts aging patterns accurately and beats a GAN-based baseline for single-volume prediction of brain aging. We also demonstrate and analyze multi-scan conditioning to incorporate subject-specific progression rates. Finally, we show that the latent progression rates in MRExtrap's linear framework correlate with disease and age-based aging patterns from previously studied structural atrophy rates. MRExtrap offers a simple and robust method for the age-based generation of 3D brain MRIs, particularly valuable in scenarios with multiple longitudinal observations.