Improving Spectral CT Image Quality Based on Channel Correlation and Self-Supervised Learning-Reference-Cited by-同舟云学术

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Improving Spectral CT Image Quality Based on Channel Correlation and Self-Supervised Learning

Published:2023 Issue: Volume:9 Page:1084-1097
ISSN:2333-9403
Container-title:IEEE Transactions on Computational Imaging
language:
Short-container-title:IEEE Trans. Comput. Imaging

Author:

Chen Xi¹^ORCID,Zhang Chaoyang¹^ORCID,Bai Ti²^ORCID,Chang Shaojie³^ORCID

Affiliation:

1. School of Information and Communications Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China

2. Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA

3. Department of Radiology, Mayo Clinic, Rochester, MN, USA

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Key R&D Project in Shaanxi Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Mathematics,Computer Science Applications,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6745852/10038323/10323100.pdf?arnumber=10323100

Reference40 articles.

1. Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing

2. U-Net: Convolutional networks for biomedical image segmentation;ronneberger;Proc Int Conf Med Image Comput Comput Assist Interv,0

3. Adaptive nonlocal means filtering based on local noise level for CT denoising

4. A Comparison of Noise and Dose in Conventional and Energy Selective Computed Tomography

5. Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples

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