| Autor: |
Lu, Yucheng, Xu, Zhixin, Choi, Moon Hyung, Kim, Jimin, Jung, Seung-Won |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
IEEE Transactions on Medical Imaging (2024) |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/TMI.2024.3405024 |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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