腹主动脉瘤发展模型的内隐神经表征

Dieuwertje Alblas, Marie‐Claude Hofman, C. Brune, K. Yeung, J. Wolterink
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引用次数: 2

摘要

腹主动脉瘤(AAAs)是腹主动脉的进行性扩张,如果不及时治疗,可能会导致致命的后果。需要基于成像的患者监测来选择符合手术修复条件的患者。在这项工作中,我们提出了一个基于隐式神经表征(INRs)的模型来模拟AAA的进展。我们将AAA墙随时间表示为有符号距离函数(SDF)的零水平集,通过对空间和时间进行操作的多层感知来估计。我们使用纵向CT数据中自动提取的分割掩码来优化INR。该网络以时空坐标为条件,表示任意时刻任意分辨率的AAA曲面。对SDF的时空梯度进行正则化,保证了AAA形状的正确插值。我们证明了该网络能够从高度不规则间隔获取的图像中产生平均表面距离在0.72至2.52 mm之间的AAA插值。结果表明,我们的模型可以随着时间的推移准确地插入AAA的形状,对于更个性化的AAA进展评估具有潜在的临床价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implicit Neural Representations for Modeling of Abdominal Aortic Aneurysm Progression
Abdominal aortic aneurysms (AAAs) are progressive dilatations of the abdominal aorta that, if left untreated, can rupture with lethal consequences. Imaging-based patient monitoring is required to select patients eligible for surgical repair. In this work, we present a model based on implicit neural representations (INRs) to model AAA progression. We represent the AAA wall over time as the zero-level set of a signed distance function (SDF), estimated by a multilayer perception that operates on space and time. We optimize this INR using automatically extracted segmentation masks in longitudinal CT data. This network is conditioned on spatiotemporal coordinates and represents the AAA surface at any desired resolution at any moment in time. Using regularization on spatial and temporal gradients of the SDF, we ensure proper interpolation of the AAA shape. We demonstrate the network's ability to produce AAA interpolations with average surface distances ranging between 0.72 and 2.52 mm from images acquired at highly irregular intervals. The results indicate that our model can accurately interpolate AAA shapes over time, with potential clinical value for a more personalised assessment of AAA progression.
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