The excessive radiation doses in the application of computed tomography (CT) technology pose a threat to the health of patients. However, applying a low radiation dose in CT can result in severe artifacts and noise in the captured images, thus affecting the diagnosis. Therefore, in this study, we investigate a dual residual convolution neural network (DRCNN) for low-dose CT (LDCT) imaging, whereby the CT images are reconstructed directly from the sinogram by integrating analytical domain transformations, thus reducing the loss of projection information. With this new framework, feature extraction is performed simultaneously on both the sinogram-domain sub-net and the image-domain sub-net, which utilize the residual shortcut networks and play a complementary role in suppressing the projection noise and reducing image error. This new DRCNN approach helps not only decrease the sinogram noise but also preserve significant structural information. The experimental results of simulated and real projection data demonstrate that our DRCNN achieve superior performance over other state-of-art methods in terms of visual inspection and quantitative metrics. For example, comparing with RED-CNN and DP-ResNet, the value of PSNR using our DRCNN is improved by nearly 3 dB and 1 dB, respectively.

中文翻译：

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用于低剂量 CT 成像的双残差卷积神经网络 (DRCNN)

计算机断层扫描（CT）技术应用中的过高辐射剂量对患者的健康构成威胁。然而，在 CT 中应用低辐射剂量会导致捕获的图像中出现严重的伪影和噪声，从而影响诊断。因此，在本研究中，我们研究了用于低剂量 CT (LDCT) 成像的双残差卷积神经网络 (DRCNN)，通过整合分析域变换，直接从正弦图重建 CT 图像，从而减少投影信息的损失. 在这个新框架中，正弦域子网和图像域子网同时进行特征提取，利用残差捷径网络，在抑制投影噪声和减少图像误差方面起到互补作用。这种新的 DRCNN 方法不仅有助于减少正弦图噪声，还有助于保留重要的结构信息。模拟和真实投影数据的实验结果表明，我们的 DRCNN 在视觉检查和定量指标方面取得了优于其他最先进方法的性能。例如，与 RED-CNN 和 DP-ResNet 相比，使用我们的 DRCNN 的 PSNR 值分别提高了近 3 dB 和 1 dB。