Low-dose x-ray computed tomography (LDCT) is desirable for reduced patient dose. This work develops new image reconstruction methods with deep learning (DL) regularization for LDCT. Our methods are based on the unrolling of a proximal forward–backward splitting (PFBS) framework with data-driven image regularization via deep neural networks. In contrast to PFBS-IR, which utilizes standard data fidelity updates via an iterative reconstruction (IR) method, PFBS-AIR involves preconditioned data fidelity updates that fuse the analytical reconstruction (AR) and IR methods in a synergistic way, i.e. fused analytical and iterative reconstruction (AIR). The results suggest that the DL-regularized methods (PFBS-IR and PFBS-AIR) provide better reconstruction quality compared to conventional methods (AR or IR). In addition, owing to the AIR, PFBS-AIR noticeably outperformed PFBS-IR and another DL-based postprocessing method, FBPConvNet.

中文翻译：

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低剂量CT通过近端向前-向后拆分进行深度学习正则化。

低剂量X射线计算机断层扫描（LDCT）对于减少患者剂量是理想的。这项工作为LDCT开发了具有深度学习（DL）正则化的新图像重建方法。我们的方法基于通过深度神经网络通过数据驱动的图像正则化展开近端向前-向后拆分（PFBS）框架。与通过迭代重建（IR）方法利用标准数据保真度更新的PFBS-IR相比，PFBS-AIR包含预处理数据保真度更新，这些条件保真度更新以协同方式融合了分析重建（AR）和IR方法，即融合的分析方法和分析方法。迭代重建（AIR）。结果表明，与常规方法（AR或IR）相比，DL规范化方法（PFBS-IR和PFBS-AIR）提供了更好的重建质量。此外，由于有了AIR，