This study investigated the potential of end-to-end deep learning tools as a
more effective substitute for FEM in predicting stress-strain fields within 2D
cross sections of arterial wall. We first proposed a U-Net based fully
convolutional neural network (CNN) to predict the von Mises stress and strain
distribution based on the spatial arrangement of calcification within arterial
wall cross-sections. Further, we developed a conditional generative adversarial
network (cGAN) to enhance, particularly from the perceptual perspective, the
prediction accuracy of stress and strain field maps for arterial walls with
various calcification quantities and spatial configurations. On top of U-Net
and cGAN, we also proposed their ensemble approaches, respectively, to further
improve the prediction accuracy of field maps. Our dataset, consisting of input
and output images, was generated by implementing boundary conditions and
extracting stress-strain field maps. The trained U-Net models can accurately
predict von Mises stress and strain fields, with structural similarity index
scores (SSIM) of 0.854 and 0.830 and mean squared errors of 0.017 and 0.018 for
stress and strain, respectively, on a reserved test set. Meanwhile, the cGAN
models in a combination of ensemble and transfer learning techniques
demonstrate high accuracy in predicting von Mises stress and strain fields, as
evidenced by SSIM scores of 0.890 for stress and 0.803 for strain.
Additionally, mean squared errors of 0.008 for stress and 0.017 for strain
further support the model's performance on a designated test set. Overall, this
study developed a surrogate model for finite element analysis, which can
accurately and efficiently predict stress-strain fields of arterial walls
regardless of complex geometries and boundary conditions.

本研究开发了一种有限元分析的替代模型，可以准确、高效地预测不考虑复杂几何形状和边界条件下的动脉壁应力应变场。