Multi-frame-based Cross-domain Denoising for Low-dose Spiral Computed Tomography

Computed tomography (CT) has been used worldwide as a non-invasive test 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 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 collected using the Radon transform. 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 third-generation 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 the multi-slice projections and the volumetric reconstructions while leveraging the over-smoothing of high-frequency information in conventional cascaded frameworks due to 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 radiologists further supported its superior performance against state-of-the-art methods in clinical practice.