基于深度学习的超低计数全身PET图像去噪方法  

Ultra-low count total-body PET image denoising based on the deep learning method

作　　者：贺钰茹 王方虎 黄衍超 路利军[3] HE Yuru;WANG Fanghu;HUANG Yanchao;LU Lijun(Big Data and Artificial Intelligence Center,Southern Medical University,Guangzhou 510515,China;Nanfang PET Center,Nanfang Hospital,Southern Medical University,Guangzhou 510515,China;School of Biomedical Engineering,Southern Medical University,Guangzhou 510515,China;Department of Nuclear Medicine,Guangdong Provincial People's Hospital,Guangdong Academy of Medical Sciences,Guangzhou 510080,China)

机构地区：[1]南方医科大学南方医院大数据与人工智能中心,广东广州510515 [2]南方医科大学南方医院PET中心,广东广州510515 [3]南方医科大学生物医学工程学院,广东广州510515 [4]广东省人民医院(广东省医学科学院)核医学科,广东广州510080

出　　处：《分子影像学杂志》2024年第9期904-912,共9页Journal of Molecular Imaging

基　　金：南方医科大学南方医院院长基金(2021C012)。

摘　　要：目的使用深度学习算法改善全身低计数正电子发射计算机断层成像(PET)的重建图像质量,探讨提出方法对不同噪声水平PET图像的去噪泛化性。方法使用MICCAI 2022 UDPET挑战赛数据集,提出分层向量量化变分自编码器(HVQ-VAE)算法对不同剂量衰减因子的低计数PET图像去噪。将高斯滤波作为基准模型,结合标准均方根误差、结构相似性、峰值信噪比3个定量指标与视觉图像结合评估算法的去噪性能。结果当低计数PET图像的剂量衰减因子为20时,经高斯滤波后图像质量整体提升13%,经HVQ-VAE模型去噪后图像质量总体提升20%;当低计数PET图像的剂量衰减因子为50时,高斯滤波后图像质量整体提升11%,HVQ-VAE模型去噪后图像质量总体提升24%;当低计数PET图像的剂量衰减因子为100时,高斯滤波后图像质量整体提升12%,HVQ-VAE模型去噪后图像质量总体提升36%。结论所提方法HVQ-VAE模型对不同噪声水平的全身低计数PET图像均有较好去噪效果,为降低患者辐射暴露风险同时保证图像质量提供了新的可能。Objective To improve the reconstructed image quality of low-count positron emission tomography(PET)imaging based on deep learning method and explore the generalization performance of the proposed method on different noise levels.Methods Using the dataset from the MICCAI 2022 UDPET Challenge,the hierarchical vector quantized variational autoencoder(HVQ-VAE)method was proposed to denoise low-count PET images with different dose reduction factors(DRFs).The denoising efficacy was quantitatively evaluated via metrics such as normalized root mean square error,structural similarity,and peak signal-to-noise ratio,as well as through visual assessments,against the Gaussian filter as baseline mothod.Results When the DRF of low-count PET images was 20,the overall image quality was improved by 13% after Gaussian filtering,and 20% after denoising by HVQ-VAE.At a DRF of 50,the proposed approach outperformed the Gaussian filter,delivering a 24% quality improvement compared to its 11%.At the DRF of 100,the HVQ-VAE method marked 36% improvement in overall image quality,as opposed to the 12% achieved with the Gaussian filter.Conclusion The HVQ-VAE method,as part of our proposed technique,has demonstrated a marked denoising effect on total-body ultra-low-count PET images across diverse noise levels.This research opens up novel avenues for reducing radiation exposure risks while ensuring maintenance of image fidelity.

关 键 词：正电子发射断层成像 图像去噪 低计数PET图像 全身PET图像 HVQ-VAE 

分 类 号：R817.4[医药卫生—影像医学与核医学] TP18[医药卫生—放射医学] TP391.41[医药卫生—临床医学]