  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 problem 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, and subjective
evaluations by two experienced radiologists further supported its superior
performance against state-of-the-art methods in clinical practice.
