#4478. Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography

August 2026publication date
Proposal available till 15-05-2025
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Journal’s subject area:
Visual Arts and Performing Arts;
Computer Science (miscellaneous);
Computer Graphics and Computer-Aided Design;
Medicine (miscellaneous);
Computer Vision and Pattern Recognition;
Software;
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Abstract:
To minimize radiation risk, dose reduction is important in the diagnostic and therapeutic applications of computed tomography (CT). The aim of this study was to compare the dose-dependent properties of a CNN-based denoising method for low-dose CT with those of other noise-reduction methods on unique CT noise-simulation images. To simulate a low-dose CT image, a Poisson noise distribution was introduced to normal-dose images while convoluting the CT unit-specific modulation transfer function. These images were denoised using the denoising network structure of CNN (DnCNN) as the general CNN model and for transfer learning. Significantly better denoising, in terms of SSIM and PSNR, was achieved by the DnCNN than by other image denoising methods, especially at the ultra-low-dose levels used to generate the 10% and 5% dose-equivalent images. In contrast, under small dose-reduction conditions, this model also led to excessive smoothing of the images. In quantitative evaluations, the CNN denoising method improved the low-dose CT and prevented over-smoothing by tailoring the CNN model.
Keywords:
Convolutional neural network; Deep learning; Denoising; Image quality; Low-dose computed tomography

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