ArticlePrecision clinical medicine2022
A selective kernel-based cycle-consistent generative adversarial network for unpaired low-dose CT denoising.
Article in Precision clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 23 citations in OpenAlex.
- RDB CycleGAN-CBAM: an integrated deep learning model for low-dose ct image denoising.Medical & biological engineering & computing · 2026Article
- A systematic review of deep learning-based denoising for low-dose computed tomography from a perceptual quality perspective.Biomedical engineering letters · 2024Review
- Weakly supervised low-dose computed tomography denoising based on generative adversarial networks.Quantitative imaging in medicine and surgery · 2024Article
- An efficient lightweight network for image denoising using progressive residual and convolutional attention feature fusion.Scientific reports · 2024Article
- CT image denoising methods for image quality improvement and radiation dose reduction.Journal of applied clinical medical physics · 2024Review
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Authors and funding
5 authors at 1 institution in 1 country.
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Abstract
Low-dose computed tomography (LDCT) denoising is an indispensable procedure in the medical imaging field, which not only improves image quality, but can mitigate the potential hazard to patients caused by routine doses. Despite the improvement in performance of the cycle-consistent generative adversarial network (CycleGAN) due to the well-paired CT images shortage, there is still a need to further reduce image noise while retaining detailed features. Inspired by the residual encoder-decoder convolutional neural network (RED-CNN) and U-Net, we propose a novel unsupervised model using CycleGAN for LDCT imaging, which injects a two-sided network into selective kernel networks (SK-NET) to adaptively select features, and uses the patchGAN discriminator to generate CT images with more detail maintenance, aided by added perceptual loss. Based on patch-based training, the experimental results demonstrated that the proposed SKFCycleGAN outperforms competing methods in both a clinical dataset and the Mayo dataset. The main advantages of our method lie in noise suppression and edge preservation.
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