Dong Mengjin, Xie Long, Das Sandhitsu R, Wang Jiancong, Wisse Laura E M, deFlores Robin, Wolk David A, Yushkevich Paul A
Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States.
Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
ArXiv. 2023 Apr 10:arXiv:2304.04673v1.
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.
对脑萎缩，尤其是海马体萎缩进行纵向评估，是针对神经退行性疾病（如阿尔茨海默病（AD））进行了充分研究的生物标志物。在临床试验中，脑萎缩进展速率的估计可用于追踪疾病修饰治疗的疗效。然而，大多数先进的测量方法是通过对MRI图像进行分割和/或可变形配准来直接计算变化的，并且可能会将头部运动或MRI伪影误报为神经退行性变，从而影响其准确性。在我们之前的研究中，我们开发了一种深度学习方法DeepAtrophy，它使用卷积神经网络来量化与时间相关的纵向MRI扫描对之间的差异。DeepAtrophy在从纵向MRI扫描中推断时间信息（如时间顺序或相对扫描间隔）方面具有很高的准确性。DeepAtrophy还提供了一个总体萎缩评分，该评分作为疾病进展和治疗疗效的潜在生物标志物表现良好。然而，DeepAtrophy不可解释，并且尚不清楚MRI中的哪些变化对进展测量有贡献。在本文中，我们提出了区域深度萎缩（RDA）方法，它将DeepAtrophy的时间推断方法与可变形配准神经网络和注意力机制相结合，该机制突出显示MRI图像中纵向变化对时间推断有贡献的区域。RDA具有与DeepAtrophy相似的预测准确性，但其额外的可解释性使其在临床环境中更易于接受，并且可能会在早期AD的临床试验中产生更敏感的疾病监测生物标志物。
