CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization-Reference-Cited by-同舟云学术

CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization

Published:2024-02 Issue:2 Volume:43 Page:745-759
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Gao Qi¹,Li Zilong²,Zhang Junping²^ORCID,Zhang Yi³^ORCID,Shan Hongming¹^ORCID

Affiliation:

1. Institute of Science and Technology for Brain-Inspired Intelligence and the MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China

2. Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China

3. School of Cyber Science and Engineering, Sichuan University, Chengdu, China

Funder

National Natural Science Foundation of China

Shanghai Sailing Program

Shanghai Municipal Science and Technology Major Project

ZJLab

Shanghai Municipal of Science and Technology Project

Sichuan Science and Technology Program

Sichuan University “From 0 to 1” Innovative Research Program

Shanghai Center for Brain Science and Brain-Inspired Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/10419117/10268250.pdf?arnumber=10268250

Cited by 14 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. Adaptive edge prior-based deep attention residual network for low-dose CT image denoising;Biomedical Signal Processing and Control;2024-12

2. FDiff-Fusion: Denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation;Information Fusion;2024-12

3. Multiple diffusion models-enhanced extremely limited-view reconstruction strategy for photoacoustic tomography boosted by multi-scale priors;Photoacoustics;2024-09

4. A systematic review of deep learning-based denoising for low-dose computed tomography from a perceptual quality perspective;Biomedical Engineering Letters;2024-08-30

5. IQAGPT: computed tomography image quality assessment with vision-language and ChatGPT models;Visual Computing for Industry, Biomedicine, and Art;2024-08-05