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.
翻译:腹膜动脉动脉瘤(AAAss)是腹膜动脉的逐渐分化,如果不加处理,可能会造成致命后果。根据成像对病人的监测需要选择有资格接受外科修复的病人。在这项工作中,我们提出了一个模型,以隐含的神经表层(INRs)为基础,以模拟AAA的进化。我们代表AAA墙,作为代号距离函数(SDF)的零水平组合,用在空间和时间上运行的多层感知估计。我们利用纵向CT数据中自动提取的分解面罩优化了这个IRN。这个网络以空间和时间坐标为条件,并且代表AAAA的表面,随时以任何理想的分辨率进行。我们利用SDF的空间和时间梯度的正规化,确保AAA的形状得到适当的内插。我们展示了网络制作AAA的内插图的能力,其平均表面距离在0.72至2.52毫米之间,从高度不定期的图像中获取。结果显示,我们的模型可以精确地轴轴轴AA形状AA。</s>