GAN-Based Architecture for Low-dose Computed Tomography Imaging Denoising

Generative Adversarial Networks (GANs) have surfaced as a revolutionary element within the domain of low-dose computed tomography (LDCT) imaging, providing an advanced resolution to the enduring issue of reconciling radiation exposure with image quality. This comprehensive review synthesizes the rap

Authors

Yunuo Wang
Ningning Yang (ORCID: https://orcid.org/0000-0002-3322-6108)
Jialin Li (ORCID: https://orcid.org/0000-0001-6930-9530)

Publication Details

Published: 2025-01-01
DOI: https://doi.org/10.4108/eai.21-11-2024.2354627
Citations: 2
Primary Topic: Medical Imaging Techniques and Applications
Type: article
Field-Weighted Citation Impact: 2.72

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article

GAN-Based Architecture for Low-dose Computed Tomography Imaging Denoising

Yunuo Wang, Ningning Yang, Jialin Li

Jan 1, 2025

2 citations

Medical Imaging Techniques and Applications

2.72

article

GAN-Based Architecture for Low-dose Computed Tomography Imaging Denoising

Yunuo Wang, Ningning Yang, Jialin Li

January 1, 2025article en

2 citations

Abstract

Openalex Percentile: Top 12%

Medical Imaging Techniques and Applications

2.72

DOI Open Access PDF OpenAlex

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GAN-Based Architecture for Low-dose Computed Tomography Imaging Denoising — Yunuo Wang, Ningning Yang, et al. · (2025) | TGRS Research Map | TGRS