BACKGROUND:The fusion of computer tomography and deep learning is an effective way of achieving improved image quality and artifact reduction in reconstructed images. OBJECTIVE:In this paper, we present two novel neural network architectures for tomographic reconstruction with reduced effects of beam hardening and electrical noise. METHODS:In the case of the proposed novel architectures, the image reconstruction step is located inside the neural networks, which allows the network to be trained by taking the mathematical model of the projections into account. This strong connection enables us to enhance the projection data and the reconstructed image together. We tested the two proposed models against three other methods on two datasets. The datasets contain physically correct simulated data, and they show strong signs of beam hardening and electrical noise. We also performed a numerical evaluation of the neural networks on the reconstructed images according to three error measurements and provided a scoring system of the methods derived from the three measures. RESULTS:The results showed the superiority of the novel architecture called TomoNet2. TomoNet2 improved the quality of the images according to the average Structural Similarity Index from 0.9372 to 0.9977 and 0.9519 to 0.9886 on the two data sets, when compared to the FBP method. This network also yielded the best results for 79.2 and 53.0 percent for the two datasets according to Peak-Signal-to-Noise-Ratio compared to the other improvement techniques. CONCLUSIONS:Our experimental results showed that the reconstruction step used in skip connections in deep neural networks improves the quality of the reconstructions. We are confident that our proposed method can be effectively applied to other datasets for tomographic purposes.

中文翻译：

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用于透射断层扫描的新型基于 U-net 的深度神经网络

背景：计算机断层扫描和深度学习的融合是提高重建图像质量和减少伪影的有效途径。目的：在本文中，我们提出了两种用于层析成像重建的新型神经网络架构，可减少光束硬化和电噪声的影响。方法：在提出的新颖架构的情况下，图像重建步骤位于神经网络内部，这允许通过考虑投影的数学模型来训练网络。这种强连接使我们能够同时增强投影数据和重建图像。我们在两个数据集上针对其他三种方法测试了两个提出的模型。数据集包含物理上正确的模拟数据，它们显示出强烈的光束硬化和电噪声迹象。我们还根据三个误差测量对重建图像上的神经网络进行了数值评估，并提供了从这三个测量得出的方法的评分系统。结果：结果显示了名为 TomoNet2 的新型架构的优越性。与 FBP 方法相比，TomoNet2 根据两个数据集的平均结构相似性指数从 0.9372 到 0.9977 和 0.9519 到 0.9886 提高了图像质量。与其他改进技术相比，根据峰值信噪比，该网络还为两个数据集产生了 79.2% 和 53.0% 的最佳结果。结论：我们的实验结果表明，深度神经网络中跳过连接中使用的重建步骤提高了重建的质量。我们相信，我们提出的方法可以有效地应用于其他数据集以进行断层扫描。