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 effectiveness 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 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）作为一种最重要的非侵入性诊断辅助手段，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驱动研究者提升重建质量，尤其是通过去除噪声和伪影。尽管以往的低剂量CT（LDCT）去噪研究已证明基于学习方法的效果，但大多数方法基于Radon变换生成的模拟数据开发。然而，真实场景与模拟域存在显著差异，且去噪与现代CT图像重建流程的联合优化仍属空白。本文针对商用第三代多层螺旋CT扫描仪，提出一种两阶段方法，以更好利用完整重建流水线实现跨域LDCT去噪。该方法充分利用多层投影与体素重建的高冗余性，同时避免了传统级联框架中的信息坍缩问题。专用设计还为工作流程提供了更清晰的解释。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。