Longitudinal assessment of brain atrophy, particularly in the hippocampus, is a well-studied biomarker for neurodegenerative diseases, such as Alzheimer's disease (AD). In clinical trials, estimation of brain progressive rates can be applied to track therapeutic efficacy of disease modifying treatments. However, most state-of-the-art measurements calculate changes directly by segmentation and/or deformable registration of MRI images, and may misreport head motion or MRI artifacts as neurodegeneration, impacting their accuracy. In our previous study, we developed a deep learning method DeepAtrophy that uses a convolutional neural network to quantify differences between longitudinal MRI scan pairs that are associated with time. DeepAtrophy has high accuracy in inferring temporal information from longitudinal MRI scans, such as temporal order or relative inter-scan interval. DeepAtrophy also provides an overall atrophy score that was shown to perform well as a potential biomarker of disease progression and treatment efficacy. However, DeepAtrophy is not interpretable, and it is unclear what changes in the MRI contribute to progression measurements. In this paper, we propose Regional Deep Atrophy (RDA), which combines the temporal inference approach from DeepAtrophy with a deformable registration neural network and attention mechanism that highlights regions in the MRI image where longitudinal changes are contributing to temporal inference. RDA has similar prediction accuracy as DeepAtrophy, but its additional interpretability makes it more acceptable for use in clinical settings, and may lead to more sensitive biomarkers for disease monitoring in clinical trials of early AD.

翻译：纵向脑萎缩评估（尤其是海马体区域）是神经退行性疾病（如阿尔茨海默病）中研究充分的生物标志物。在临床试验中，脑部进展率的估算可用于追踪疾病修饰疗法的疗效。然而，当前最先进的测量方法主要通过MRI图像的分割和/或可变形配准直接计算变化，可能将头部运动或MRI伪影误报为神经退行性改变，从而影响其准确性。我们此前的研究开发了深度学习方法DeepAtrophy，利用卷积神经网络量化纵向MRI扫描对中与时间相关的差异。DeepAtrophy在推断纵向MRI扫描的时间信息（如时间顺序或相对扫描间隔）方面具有高精度，同时提供整体萎缩评分，该评分已被证明可作为疾病进展和疗效的潜在生物标志物。然而，DeepAtrophy缺乏可解释性，无法明确MRI中的哪些变化贡献于进展测量。本文提出区域深度萎缩方法，将DeepAtrophy的时间推断方法与可变形配准神经网络及注意力机制相结合，突出显示MRI图像中纵向变化对时间推断贡献的区域。RDA具有与DeepAtrophy相似的预测精度，但其增强的可解释性使其更适用于临床环境，并可能为早期阿尔茨海默病临床试验中的疾病监测提供更灵敏的生物标志物。