Better Cone-Beam CT Artifact Correction via Spatial and Channel Reconstruction Convolution Based on Unsupervised Adversarial Diffusion Models

Better Cone-Beam CT Artifact Correction via Spatial and Channel Reconstruction Convolution Based on Unsupervised Adversarial Diffusion Models

Cone-Beam Computed Tomography (CBCT) holds significant clinical value in image-guided radiotherapy (IGRT). However, CBCT images of low-density soft tissues are often plagued with artifacts and noise, which can lead to missed diagnoses and misdiagnoses. We propose a new unsupervised CBCT image artifa...

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Bibliographic Details
Main Authors: Guoya Dong, Yutong He, Xuan Liu, Jingjing Dai, Yaoqin Xie, Xiaokun Liang
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Bioengineering
Subjects:
CBCT reconstruction
diffusion model
deep learning
Online Access:https://www.mdpi.com/2306-5354/12/2/132
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