ArticleMedical physics2025
Noise-augmented deep denoising: A method to boost CT image denoising networks.
Article in Medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Noise-augmented deep denoising: A method to boost CT image denoising networks.Medical physics · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundDenoising low dose computed tomography (CT) images can have great advantages for the aim of minimizing the radiation risk of the patients, as it can help lower the effective dose to the patient while providing constant image quality. In recent years, deep denoising methods became a popular way to accomplish this task. Conventional deep denoising algorithms, however, cannot handle the correlation between neighboring pixels or voxels very well, because the noise structure in CT is a resultant of the global attenuation properties of the patient and because the receptive field of most denoising approaches is rather small. PURPOSE: The purpose of this study is to improve existing denoising networks, by providing them additional information about the image noise.
methodsWe here propose to generate
resultsIn all cases tested, the denoising networks strongly benefit from the noise augmentation. Noise artifacts that are being misinterpreted by the original networks as being anatomical structures, are correctly removed by the NADD version of the same networks. The more noise images are provided, the better the performance.
conclusionsProviding additional simulated noise realizations helps to significantly improve the performance of CT image denoising networks.
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Registered trials
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