Deep residual constrained reconstruction via learned convolutional sparse coding for low-dose CT imaging

Author:

Liu Jin,Zhang Tingyu,Kang Yanqin,Wang Yong,Zhang YikunORCID,Hu DianlinORCID,Chen Yang

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Health Informatics,Signal Processing,Biomedical Engineering

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Artifact suppression for sparse view CT via transformer-based generative adversarial network;Biomedical Signal Processing and Control;2024-09

2. Innovative Noise Extraction and Denoising in Low-Dose CT Using a Supervised Deep Learning Framework;Electronics;2024-08-12

3. Parallel processing model for low-dose computed tomography image denoising;Visual Computing for Industry, Biomedicine, and Art;2024-06-12

4. Artifact Removal from Low-Dose Multi-Energy CT Images via GAN with Joint Loss;2024 IEEE International Conference on Computational Electromagnetics (ICCEM);2024-04-15

5. Weighted sparse constraint-based reconstruction networks for sparse view CT imaging;Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023);2024-03-25

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