计算机断层扫描 (CT) 已在全世界范围内用作辅助诊断的非侵入性检查。然而，X 射线暴露的电离性质引起了人们对癌症等潜在健康风险的担忧。对较低辐射剂量的渴望促使研究人员提高重建质量。尽管之前关于低剂量计算机断层扫描（LDCT）去噪的研究已经证明了基于学习的方法的有效性，但大多数都是在模拟数据上开发的。然而，现实世界的场景与模拟域有很大不同，特别是在使用多层螺旋扫描仪几何结构时。本文提出了一种适用于商用多层螺旋 CT 扫描仪的两阶段方法，该方法可以更好地利用完整的重建流程来跨不同领域进行 LDCT 去噪。我们的方法充分利用了多切片投影和体积重建的高冗余，同时利用了传统级联框架中因过度去噪而导致的过度平滑问题。专用设计还提供了对数据流的更明确的解释。对各种数据集的大量实验表明，所提出的方法可以在不影响空间分辨率的情况下消除高达 70% 的噪声，而两位经验丰富的放射科医生的主观评估进一步支持了其在临床实践中相对于最先进方法的优越性能。代码可在 https://github.com/YCL92/TMD-LDCT 获取。

Computed tomography (CT) has been used worldwide as a non-invasive test to assist in diagnosis. However, the ionizing nature of X-ray exposure raises concerns about potential health risks such as cancer. The desire for lower radiation doses has driven researchers to improve reconstruction quality. Although previous studies on low-dose computed tomography (LDCT) denoising have demonstrated the effectiveness of learning-based methods, most were developed on the simulated data. However, the real-world scenario differs significantly from the simulation domain, especially when using the multi-slice spiral scanner geometry. This paper proposes a two-stage method for the commercially available multi-slice spiral CT scanners that better exploits the complete reconstruction pipeline for LDCT denoising across different domains. Our approach makes good use of the high redundancy of multi-slice projections and the volumetric reconstructions while leveraging the over-smoothing issue in conventional cascaded frameworks caused by aggressive denoising. The dedicated design also provides a more explicit interpretation of the data flow. Extensive experiments on various datasets showed that the proposed method could remove up to 70% of noise without compromised spatial resolution, while subjective evaluations by two experienced radiologists further supported its superior performance against state-of-the-art methods in clinical practice. Code is available at https://github.com/YCL92/TMD-LDCT.