标题： 基于深度神经网络的腹主动脉瘤二维超声弹性成像 显示英文标题

标题： 2D Ultrasound Elasticity Imaging of Abdominal Aortic Aneurysms Using Deep Neural Networks

摘要： 腹主动脉瘤（AAA）由于其破裂的潜在风险，对临床构成重大威胁，这种破裂通常无症状但可能致命。 尽管最大直径常用于风险评估，但仅凭直径是不够的，因为它无法捕捉血管壁基础材料的特性，而这些特性在决定破裂风险中起着关键作用。 为克服这一限制，我们提出了一种基于深度学习的2D超声弹性成像框架。 利用有限元模拟，我们生成了具有相应模量分布的位移场多样化数据集。 我们使用U-Net架构和归一化均方误差（NMSE）训练一个模型，以从位移场的轴向和横向分量推断空间模量分布。 该模型在三个实验领域进行了评估：来自3D COMSOL仿真的数字幻象数据、使用生物力学不同的血管模型进行的物理幻象实验以及AAA患者的临床超声检查。 我们的仿真结果表明，所提出的深度学习模型能够重建模量分布，达到0.73%的NMSE得分。 同样，在幻象数据中，预测的模量比与预期值非常接近，证实了模型在幻象数据上的泛化能力。 我们将这种方法与一种迭代方法进行比较，结果显示性能相当但计算时间更长。 相比之下，深度学习方法可以从超声图像中快速有效地估计组织硬度，这有助于在不进行侵入性操作的情况下评估AAA破裂的风险。 显示英文摘要

摘要： Abdominal aortic aneurysms (AAA) pose a significant clinical risk due to their potential for rupture, which is often asymptomatic but can be fatal. Although maximum diameter is commonly used for risk assessment, diameter alone is insufficient as it does not capture the properties of the underlying material of the vessel wall, which play a critical role in determining the risk of rupture. To overcome this limitation, we propose a deep learning-based framework for elasticity imaging of AAAs with 2D ultrasound. Leveraging finite element simulations, we generate a diverse dataset of displacement fields with their corresponding modulus distributions. We train a model with U-Net architecture and normalized mean squared error (NMSE) to infer the spatial modulus distribution from the axial and lateral components of the displacement fields. This model is evaluated across three experimental domains: digital phantom data from 3D COMSOL simulations, physical phantom experiments using biomechanically distinct vessel models, and clinical ultrasound exams from AAA patients. Our simulated results demonstrate that the proposed deep learning model is able to reconstruct modulus distributions, achieving an NMSE score of 0.73\%. Similarly, in phantom data, the predicted modular ratio closely matches the expected values, affirming the model's ability to generalize to phantom data. We compare our approach with an iterative method which shows comparable performance but higher computation time. In contrast, the deep learning method can provide quick and effective estimates of tissue stiffness from ultrasound images, which could help assess the risk of AAA rupture without invasive procedures.