Zero-Shot Low-dose CT Denoising via Sinogram Flicking

• URL: http://arxiv.org/abs/2504.07927v1
• Date: Thu, 10 Apr 2025 17:42:01 GMT
• Title: Zero-Shot Low-dose CT Denoising via Sinogram Flicking
• Authors: Yongyi Shi, Ge Wang,
• Abstract summary: We propose a zero-shot low-dose CT imaging method based on sinogram flicking.<n>By randomly swapping portions of the conjugate X-rays in the sinogram domain, we generate a large set of sinograms with consistent content but differing noise patterns.<n>We train the network on pairs of sinograms with the same content but different noise distributions using a lightweight model adapted from ZS-NSN.
• Score: 5.101848799297469
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many low-dose CT imaging methods rely on supervised learning, which requires a large number of paired noisy and clean images. However, obtaining paired images in clinical practice is challenging. To address this issue, zero-shot self-supervised methods train denoising networks using only the information within a single image, such as ZS-N2N. However, these methods often employ downsampling operations that degrade image resolution. Additionally, the training dataset is inherently constrained to the image itself. In this paper, we propose a zero-shot low-dose CT imaging method based on sinogram flicking, which operates within a single image but generates many copies via random conjugate ray matching. Specifically, two conjugate X-ray pencil beams measure the same path; their expected values should be identical, while their noise levels vary during measurements. By randomly swapping portions of the conjugate X-rays in the sinogram domain, we generate a large set of sinograms with consistent content but varying noise patterns. When displayed dynamically, these sinograms exhibit a flickering effect due to their identical structural content but differing noise patterns-hence the term sinogram flicking. We train the network on pairs of sinograms with the same content but different noise distributions using a lightweight model adapted from ZS-NSN. This process is repeated to obtain the final results. A simulation study demonstrates that our method outperforms state-of-the-art approaches such as ZS-N2N.

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