Yuxin Xue;Lei Bi;Yige Peng;Michael Fulham;David Dagan Feng;Jinman Kim
{"title":"PET Synthesis via Self-Supervised Adaptive Residual Estimation Generative Adversarial Network","authors":"Yuxin Xue;Lei Bi;Yige Peng;Michael Fulham;David Dagan Feng;Jinman Kim","doi":"10.1109/TRPMS.2023.3339173","DOIUrl":null,"url":null,"abstract":"Positron emission tomography (PET) is a widely used, highly sensitive molecular imaging in clinical diagnosis. There is interest in reducing the radiation exposure from PET but also maintaining adequate image quality. Recent methods using convolutional neural networks (CNNs) to generate synthesized high-quality PET images from “low-dose” counterparts have been reported to be “state-of-the-art” for low-to-high-image recovery methods. However, these methods are prone to exhibiting discrepancies in texture and structure between synthesized and real images. Furthermore, the distribution shift between low-dose PET and standard PET has not been fully investigated. To address these issues, we developed a self-supervised adaptive residual estimation generative adversarial network (SS-AEGAN). We introduce 1) an adaptive residual estimation mapping mechanism, AE-Net, designed to dynamically rectify the preliminary synthesized PET images by taking the residual map between the low-dose PET and synthesized output as the input and 2) a self-supervised pretraining strategy to enhance the feature representation of the coarse generator. Our experiments with a public benchmark dataset of total-body PET images show that SS-AEGAN consistently outperformed the state-of-the-art synthesis methods with various dose reduction factors.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 4","pages":"426-438"},"PeriodicalIF":4.6000,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Radiation and Plasma Medical Sciences","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10342759/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
Positron emission tomography (PET) is a widely used, highly sensitive molecular imaging in clinical diagnosis. There is interest in reducing the radiation exposure from PET but also maintaining adequate image quality. Recent methods using convolutional neural networks (CNNs) to generate synthesized high-quality PET images from “low-dose” counterparts have been reported to be “state-of-the-art” for low-to-high-image recovery methods. However, these methods are prone to exhibiting discrepancies in texture and structure between synthesized and real images. Furthermore, the distribution shift between low-dose PET and standard PET has not been fully investigated. To address these issues, we developed a self-supervised adaptive residual estimation generative adversarial network (SS-AEGAN). We introduce 1) an adaptive residual estimation mapping mechanism, AE-Net, designed to dynamically rectify the preliminary synthesized PET images by taking the residual map between the low-dose PET and synthesized output as the input and 2) a self-supervised pretraining strategy to enhance the feature representation of the coarse generator. Our experiments with a public benchmark dataset of total-body PET images show that SS-AEGAN consistently outperformed the state-of-the-art synthesis methods with various dose reduction factors.
正电子发射断层扫描(PET)是一种广泛应用于临床诊断的高灵敏度分子成像技术。人们希望减少 PET 的辐射量,同时又能保持足够的图像质量。据报道,最近使用卷积神经网络(CNN)从 "低剂量 "对应图像生成合成高质量 PET 图像的方法是 "最先进的 "低剂量到高质量图像复原方法。然而,这些方法容易在合成图像和真实图像之间出现纹理和结构差异。此外,低剂量 PET 和标准 PET 之间的分布偏移尚未得到充分研究。为了解决这些问题,我们开发了自监督自适应残差估计生成对抗网络(SS-AEGAN)。我们引入了:1)自适应残差估计映射机制 AE-Net,旨在将低剂量 PET 和合成输出之间的残差映射作为输入,动态修正初步合成的 PET 图像;2)自监督预训练策略,以增强粗生成器的特征表示。我们使用公开的全身 PET 图像基准数据集进行的实验表明,SS-AEGAN 的性能始终优于使用各种剂量降低系数的最先进合成方法。