Unpaired low-dose CT denoising via an improved cycle-consistent adversarial network with attention ensemble

Yin, Zhixian; Xia, Kewen; Wang, Sijie; He, Ziping; Zhang, Jiangnan; Zu, Baokai

doi:10.1007/s00371-022-02599-8

Cited by 8 publications

(2 citation statements)

References 55 publications

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“…Thus, we can estimate the target values (i.e., denoised content) from repeated noisy measurements (i.e., noisy multi frame images). The attention gate was applied to image restoration tasks, such as denoising [68]- [70], superresolution [71], inpainting [72], dehazing [73], and image enhancement [74]. It improved the reconstruction performance by employing useful features and ignoring irrelevant features, and it effectively suppressed noise when the attention gate was included [69].…”

Section: Discussionmentioning

confidence: 99%

MM-Net: Multiframe and Multimask-Based Unsupervised Deep Denoising for Low-Dose Computed Tomography

Jeon

Kim

Choi

2023

IEEE Trans. Radiat. Plasma Med. Sci.

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Low-dose computed tomography (LDCT) is crucial due to the risk of radiation exposure to patients. However, the high noise level in LDCT images may reduce the image quality, leading to a less accurate diagnosis. Deep learning technology, especially supervised methods, has recently been widely accepted as a powerful tool for LDCT image denoising tasks. However, supervised methods require numerous paired datasets of LDCT and high-quality pristine CT images, which are rarely available in real-world clinical scenarios. This study presents an unsupervised learning-based framework called MM-Net, consisting of two training steps for a volumetric LDCT denoising task. In the two-step training approach, we first train the initial denoising network multi-scale attention U-Net (MSAU-Net) in a self-supervised manner to predict the noise-suppressed center slice with a neighboring multi-slice input. The second training step aims to train the U-Net-based final denoiser based on the pre-trained MSAU-Net to improve the image quality by introducing new multi-patch and multi-mask matching loss. Qualitative visual inspection and quantitative measures across texturally different domains of clinical and animal data reveal that the proposed MM-Net outperformed all competing state-ofthe-art unsupervised algorithms. The unsupervised method also achieved denoising performance comparable to the representative supervised methods trained with ground truth images.

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Section: Discussionmentioning

confidence: 99%

MM-Net: Multiframe and Multimask-Based Unsupervised Deep Denoising for Low-Dose Computed Tomography

Jeon

Kim

Choi

2023

IEEE Trans. Radiat. Plasma Med. Sci.

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“…The second is to improve the image quality by training the U-Net-based final denoiser using the pre-trained MSAU-Net in the first training step. Yin et al [41] proposed improved Cycle-Consistent Adversarial Networks (CycleGAN) for LDCT image denoising that included a perceptual loss, and a generator network based on U-Net, in which the skip connection between the encoder and decoder path has been reconstructed.…”

Section: Background and Related Workmentioning

confidence: 99%

Variant Wasserstein Generative Adversarial Network Applied on Low Dose CT Image Denoising

Mahmoud¹,

Sayed²,

Mohamed³

2023

Computers, Materials &Amp; Continua

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Computed Tomography (CT) images have been extensively employed in disease diagnosis and treatment, causing a huge concern over the dose of radiation to which patients are exposed. Increasing the radiation dose to get a better image may lead to the development of genetic disorders and cancer in the patients; on the other hand, decreasing it by using a Low-Dose CT (LDCT) image may cause more noise and increased artifacts, which can compromise the diagnosis. So, image reconstruction from LDCT image data is necessary to improve radiologists' judgment and confidence. This study proposed three novel models for denoising LDCT images based on Wasserstein Generative Adversarial Network (WGAN). They were incorporated with different loss functions, including Visual Geometry Group (VGG), Structural Similarity Loss (SSIM), and Structurally Sensitive Loss (SSL), to reduce noise and preserve important information on LDCT images and investigate the effect of different types of loss functions. Furthermore, experiments have been conducted on the Graphical Processing Unit (GPU) and Central Processing Unit (CPU) to compare the performance of the proposed models. The results demonstrated that images from the proposed WGAN-SSIM, WGAN-VGG-SSIM, and WGAN-VGG-SSL were denoised better than those from state-of-the-art models (WGAN, WGAN-VGG, and SMGAN) and converged to a stable equilibrium compared with WGAN and WGAN-VGG. The proposed models are effective in reducing noise, suppressing artifacts, and maintaining informative structure and texture details, especially WGAN-VGG-SSL which achieved a high peak-signalto-noise ratio (PNSR) on both GPU (26.1336) and CPU (25.8270). The average accuracy of WGAN-VGG-SSL outperformed that of the state-ofthe-art methods by 1 percent. Experiments prove that the WGAN-VGG-SSL is more stable than the other models on both GPU and CPU.

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De-noising of Low Dose CT Liver Images Using Improved Discrete Wavelet Transform

Heartlin Maria,

Kayalvizhi,

Keren Evangeline

et al. 2024

IFIP Advances in Information and Communication Technology

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Unpaired low-dose CT denoising via an improved cycle-consistent adversarial network with attention ensemble

Cited by 8 publications

References 55 publications

MM-Net: Multiframe and Multimask-Based Unsupervised Deep Denoising for Low-Dose Computed Tomography

MM-Net: Multiframe and Multimask-Based Unsupervised Deep Denoising for Low-Dose Computed Tomography

Variant Wasserstein Generative Adversarial Network Applied on Low Dose CT Image Denoising

De-noising of Low Dose CT Liver Images Using Improved Discrete Wavelet Transform

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