CT理论与应用研究2026，Vol.35Issue(1)：36-47,12.DOI:10.15953/j.ctta.2025.171
基于最优传输网络的光子计数CT投影降噪方法
Photon-counting CT Projection Denoising Method Based on Optimal Transport Network
摘要
Abstract
In photon-counting computerized tomography(PCCT),the detector can only receive partial photon energy within a single energy channel,resulting in limited photon counting rates and a significantly reduced signal-to-noise ratio of the projection data.Aiming at the limitation that strongly supervised denoising methods rely on large-scale paired datasets and to address the issue that weakly supervised methods using unpaired data have insufficient denoising performance,this study proposes a weakly supervised projection denoising method based on an optimal transport network.The method first adaptively matches the noise and reference distributions by constructing an optimal transport constraint term for projection data consistency.Second,an optimal transport generative adversarial network framework integrated with attention mechanisms was designed to synchronously optimize noise suppression and detail recovery capabilities under unpaired training conditions.Finally,the framework was used to process noisy projections and perform image reconstruction,verifying the feature consistency transfer from the projection to the image domain.In experiments,compared with mainstream denoising methods,the proposed method achieved a peak signal-to-noise ratio improvement of 0.47 dB and increased the structural similarity from 0.75 to 0.81 on the PCCT projection dataset.This study provides a robust denoising solution for PCCT imaging that does not require precisely paired datasets.关键词
光子计数CT/最优传输/深度学习/投影降噪Key words
photon-counting CT/optimal transport/deep learning/projection denoising分类
信息技术与安全科学引用本文复制引用
李思宇,梁宁宁,郑治中,蔡爱龙,李磊,闫镔..基于最优传输网络的光子计数CT投影降噪方法[J].CT理论与应用研究,2026,35(1):36-47,12.基金项目
国家自然科学基金(数据驱动下能谱CT成像的噪声处理与最优传输网络优化(62271504)) （数据驱动下能谱CT成像的噪声处理与最优传输网络优化(62271504）
中原科技创新领军人才项目(244200510015). （244200510015）
