MLNAN: Multi-level noise-aware network for low-dose CT imaging implemented with constrained cycle Wasserstein generative adversarial networks

Huang, Zhenxing; Li, Wenbo; Geng, Meng-xiao; Liu, Zhou; Zhang, Qiyang; Jin, Yuxi; Wu, Ruodai; Quan, Guotao; Liang, Dong; Hu, Zhanli; Zhang, Na

doi:10.1016/j.artmed.2023.102609

Cited by 4 publications

(1 citation statement)

References 55 publications

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“…Encoder-decoder networks are widely used in image processing [18][19][20][21][35][36][37]. The encoder extracts the high-level semantic information of the image and the decoder recovers the low-level detail information of the image.…”

Section: Encoder-decoder Networkmentioning

confidence: 99%

Irregular Feature Enhancer for Low-dose CT Denoising

Deng,

Hu,

et al. 2024

Preprint

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So far, deep learning based networks have been wildly applied in Low-Dose Computed Tomography (LDCT) image denoising. However, they usually adopt symmetric convolution to achieve regular feature extraction, but cannot effectively extract irregular features. Therefore, in this paper, an Irregular Feature Enhancer (IFE) focusing on effectively extracting irregular features is proposed by combining Symmetric-Asymmetric-Synergy Convolution Module (SASCM) with a hybrid loss module. Rather than simply stacking symmetric convolution layers used in traditional deep learning based networks, SASCM jointly utilizes symmetric and asymmetric convolution layers so as to effectively extract irregular tissue information of the image. In addition, the hybrid loss module is proposed to guide IFE to further mine the intrinsic feature information of the image from three perspectives: pixel point, high-level feature space, and gradient. The ablation experiments demonstrate the effectiveness and feasibility of SASCM and the hybrid loss. The quantitative experimental results also show that compared with several related LDCT denoising methods, the proposed IFE performs the best in terms of PSNR and SSIM. Furthermore, it can be observed from the qualitative visualization that the proposed IFE can recover the best image detail structure information among the compared methods.

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Section: Encoder-decoder Networkmentioning

confidence: 99%

Irregular Feature Enhancer for Low-dose CT Denoising

Deng,

Hu,

et al. 2024

Preprint

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Noise‐assisted hybrid attention networks for low‐dose PET and CT denoising

Xue,

Yao,

Teng

2024

Medical Physics

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BackgroundPositron emission tomography (PET) and computed tomography (CT) play a vital role in tumor‐related medical diagnosis, assessment, and treatment planning. However, full‐dose PET and CT pose the risk of excessive radiation exposure to patients, whereas low‐dose images compromise image quality, impacting subsequent tumor recognition and disease diagnosis.PurposeTo solve such problems, we propose a Noise‐Assisted Hybrid Attention Network (NAHANet) to reconstruct full‐dose PET and CT images from low‐dose PET (LDPET) and CT (LDCT) images to reduce patient radiation risks while ensuring the performance of subsequent tumor recognition.MethodsNAHANet contains two branches: the noise feature prediction branch (NFPB) and the cascaded reconstruction branch. Among them, NFPB providing noise features for the cascade reconstruction branch. The cascaded reconstruction branch comprises a shallow feature extraction module and a reconstruction module which contains a series of cascaded noise feature fusion blocks (NFFBs). Among these, the NFFB fuses the features extracted from low‐dose images with the noise features obtained by NFPB to improve the feature extraction capability. To validate the effectiveness of the NAHANet method, we performed experiments using two public available datasets: the Ultra‐low Dose PET Imaging Challenge dataset and Low Dose CT Grand Challenge dataset.ResultsAs a result, the proposed NAHANet achieved higher performance on common indicators. For example, on the CT dataset, the PSNR and SSIM indicators were improved by 4.1 dB and 0.06 respectively, and the rMSE indicator was reduced by 5.46 compared with the LDCT; on the PET dataset, the PSNR and SSIM was improved by 3.37 dB and 0.02, and the rMSE was reduced by 9.04 compared with the LDPET.ConclusionsThis paper proposes a transformer‐based denoising algorithm, which utilizes hybrid attention to extract high‐level features of low dose images and fuses noise features to optimize the denoising performance of the network, achieving good performance improvements on low‐dose CT and PET datasets.

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Short-axis PET image quality improvement based on a uEXPLORER total-body PET system through deep learning

Huang,

Li,

et al. 2023

Eur J Nucl Med Mol Imaging

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MLNAN: Multi-level noise-aware network for low-dose CT imaging implemented with constrained cycle Wasserstein generative adversarial networks

Cited by 4 publications

References 55 publications

Irregular Feature Enhancer for Low-dose CT Denoising

Irregular Feature Enhancer for Low-dose CT Denoising

Noise‐assisted hybrid attention networks for low‐dose PET and CT denoising

Short-axis PET image quality improvement based on a uEXPLORER total-body PET system through deep learning

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