Cross-domain Denoising for Low-dose Multi-frame Spiral Computed Tomography
This addresses the health risks from radiation in CT scans by improving reconstruction quality for clinical practice, though it appears incremental as it builds on existing learning-based methods with domain-specific adaptations.
The paper tackled the problem of low-dose computed tomography (LDCT) denoising in real-world multi-slice spiral scanners, proposing a two-stage method that removed up to 70% of noise without compromising spatial resolution, as validated by radiologists against state-of-the-art methods.
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.