Ultra-low Dose CT Image Denoising based on Conditional Denoising Diffusion Probabilistic model-Reference-Cited by-同舟云学术

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Ultra-low Dose CT Image Denoising based on Conditional Denoising Diffusion Probabilistic model

Published:2022-10 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)
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Author:

Li Qiwei¹,Li Chen¹,Yan Chenggong²,Li Xiaomei²,Li Haixia³,Zhang Tianjing⁴,Song Hui⁵,Schaffert Roman⁶,Yu Weimin⁶,Fan Yu⁶,Ye Jianwei⁶,Chen Hao⁶

Affiliation:

1. Xi’an Jiaotong University

2. Southern Medical University,Nanfang Hospital,Department of Medical Imaging Center

3. Philips Healthcare,Guangzhou,China

4. NVIDIA Corporation,Shenzhen,China

5. University of PAP,School of Information, Engineering

6. Jiangsu JITRI Sioux Technologies Co., Ltd.

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10089804/10089998/10090098.pdf?arnumber=10090098

Reference28 articles.

1. Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss

2. 3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network

3. Generative Adversarial Networks for Noise Reduction in Low-Dose CT

4. Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction

5. Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network

Cited by 3 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. Generation model meets swin transformer for unsupervised low-dose CT reconstruction;Machine Learning: Science and Technology;2024-04-04

2. DiffusionDCI: A Novel Diffusion-Based Unified Framework for Dynamic Full-Field OCT Image Generation and Segmentation;IEEE Access;2024

3. Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals;Korean Journal of Radiology;2024

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