{"title":"用于不同计数水平低计数 PET 去噪的统一噪声感知网络","authors":"Huidong Xie;Qiong Liu;Bo Zhou;Xiongchao Chen;Xueqi Guo;Hanzhong Wang;Biao Li;Axel Rominger;Kuangyu Shi;Chi Liu","doi":"10.1109/TRPMS.2023.3334105","DOIUrl":null,"url":null,"abstract":"As positron emission tomography (PET) imaging is accompanied by substantial radiation exposure and cancer risk, reducing radiation dose in PET scans is an important topic. However, low-count PET scans often suffer from high-image noise, which can negatively impact image quality and diagnostic performance. Recent advances in deep learning have shown great potential for recovering underlying signal from noisy counterparts. However, neural networks trained on a specific noise level cannot be easily generalized to other noise levels due to different noise amplitude and variances. To obtain optimal denoised results, we may need to train multiple networks using data with different noise levels. But this approach may be infeasible in reality due to limited data availability. Denoising dynamic PET images presents additional challenge due to tracer decay and continuously changing noise levels across dynamic frames. To address these issues, we propose a unified noise-aware network (UNN) that combines multiple subnetworks with varying denoising power to generate optimal denoised results regardless of the input noise levels. Evaluated using large-scale data from two medical centers with different vendors, presented results showed that the UNN can consistently produce promising denoised results regardless of input noise levels, and demonstrate superior performance over networks trained on single noise level data, especially for extremely low-count data.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 4","pages":"366-378"},"PeriodicalIF":4.6000,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10323300","citationCount":"0","resultStr":"{\"title\":\"Unified Noise-Aware Network for Low-Count PET Denoising With Varying Count Levels\",\"authors\":\"Huidong Xie;Qiong Liu;Bo Zhou;Xiongchao Chen;Xueqi Guo;Hanzhong Wang;Biao Li;Axel Rominger;Kuangyu Shi;Chi Liu\",\"doi\":\"10.1109/TRPMS.2023.3334105\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As positron emission tomography (PET) imaging is accompanied by substantial radiation exposure and cancer risk, reducing radiation dose in PET scans is an important topic. 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引用次数: 0
摘要
正电子发射断层扫描(PET)成像伴随着大量的辐射暴露和癌症风险,因此降低 PET 扫描的辐射剂量是一个重要的课题。然而,低计数 PET 扫描往往存在高图像噪声,这会对图像质量和诊断性能产生负面影响。深度学习的最新进展表明,从噪声对应图像中恢复底层信号具有巨大潜力。然而,由于噪声的振幅和方差不同,在特定噪声水平上训练的神经网络不能轻易推广到其他噪声水平。为了获得最佳的去噪结果,我们可能需要使用不同噪声水平的数据来训练多个网络。但由于数据可用性有限,这种方法在现实中可能并不可行。由于示踪剂衰减和动态帧中不断变化的噪声水平,动态 PET 图像的去噪面临更多挑战。为了解决这些问题,我们提出了一种统一噪声感知网络(UNN),它结合了多个具有不同去噪能力的子网络,无论输入噪声水平如何,都能生成最佳的去噪结果。我们使用来自两个不同供应商的医疗中心的大规模数据进行了评估,结果表明,无论输入噪声水平如何,统一噪声感知网络都能始终如一地生成令人满意的去噪结果,而且其性能优于在单一噪声水平数据上训练的网络,特别是对于极低计数的数据。
Unified Noise-Aware Network for Low-Count PET Denoising With Varying Count Levels
As positron emission tomography (PET) imaging is accompanied by substantial radiation exposure and cancer risk, reducing radiation dose in PET scans is an important topic. However, low-count PET scans often suffer from high-image noise, which can negatively impact image quality and diagnostic performance. Recent advances in deep learning have shown great potential for recovering underlying signal from noisy counterparts. However, neural networks trained on a specific noise level cannot be easily generalized to other noise levels due to different noise amplitude and variances. To obtain optimal denoised results, we may need to train multiple networks using data with different noise levels. But this approach may be infeasible in reality due to limited data availability. Denoising dynamic PET images presents additional challenge due to tracer decay and continuously changing noise levels across dynamic frames. To address these issues, we propose a unified noise-aware network (UNN) that combines multiple subnetworks with varying denoising power to generate optimal denoised results regardless of the input noise levels. Evaluated using large-scale data from two medical centers with different vendors, presented results showed that the UNN can consistently produce promising denoised results regardless of input noise levels, and demonstrate superior performance over networks trained on single noise level data, especially for extremely low-count data.