Computed tomography (CT) has been used worldwide for decades as one of the most important non-invasive tests in assisting diagnosis. However, the ionizing nature of X-ray exposure raises concerns about potential health risks such as cancer. The desire for lower radiation dose has driven researchers to improve the reconstruction quality, especially by removing noise and artifacts. Although previous studies on low-dose computed tomography (LDCT) denoising have demonstrated the potential of learning-based methods, most of them were developed on the simulated data collected using Radon transform. However, the real-world scenario significantly differs from the simulation domain, and the joint optimization of denoising with the modern CT image reconstruction pipeline is still missing. In this paper, for the commercially available third-generation multi-slice spiral CT scanners, we propose a two-stage method that better exploits the complete reconstruction pipeline for LDCT denoising across different domains. Our method makes good use of the high redundancy of both the multi-slice projections and the volumetric reconstructions while avoiding the collapse of information in conventional cascaded frameworks. The dedicated design also provides a clearer interpretation of the workflow. Through extensive evaluations, we demonstrate its superior performance against state-of-the-art methods.

翻译：计算机断层扫描（CT）作为辅助诊断中最重要的非侵入性检查手段之一，已在全球范围内应用数十年。然而，X射线辐射的电离特性引发了人们对癌症等潜在健康风险的担忧。降低辐射剂量的需求促使研究人员致力于提升重建质量，特别是通过消除噪声和伪影。尽管针对低剂量计算机断层扫描（LDCT）去噪的先前研究已展现出基于学习方法的潜力，但多数方法是在利用拉东变换采集的模拟数据上开发的。然而，真实场景与模拟域之间存在显著差异，且去噪与当代CT图像重建流程的联合优化仍属空白。针对商业化的第三代多层螺旋CT扫描仪，本文提出了一种两阶段方法，能够更充分地利用完整重建流程实现跨域LDCT去噪。本方法充分利用了多层投影与体素重建的高度冗余性，同时避免了传统级联框架中信息崩塌的问题。该专用设计还提供了更清晰的工作流可解释性。通过广泛评估，我们证明了该方法相较于现有最优方法的优越性能。