{"title":"通过图像空间洗牌 U-Net 和身体采样的全身超低剂量 PET 重构方法","authors":"Gaoyu Chen;Sheng Liu;Wenxiang Ding;Li Lv;Chen Zhao;Fenghua Weng;Yong Long;Yunlong Zan;Qiu Huang","doi":"10.1109/TRPMS.2023.3333839","DOIUrl":null,"url":null,"abstract":"Low-dose positron emission tomography (PET) reconstruction algorithms manage to reduce the injected dose and/or scanning time in PET examination while maintaining the image quality, and thus has been extensively studied. In this article, we proposed a novel ultralow-dose reconstruction method for total-body PET. Specifically, we developed a deep learning model named ISS-Unet based on U-Net and introduced 3-D PixelUnshuffle/PixelShuffle pair in image space to reduce the training time and GPU memory. We then introduced two body sampling methods in the training patch preparation step to improve the training efficiency and local metrics. We also reported the misalignment artifacts that were often neglected in 2-D training. The proposed method was evaluated on the MICCAI 2022 Ultralow-Dose PET Imaging Challenge dataset and won the first prize in the first-round competition according to the comprehensive score combining global and local metrics. In this article, we disclosed the implementation details of the proposed method followed by the comparison results with three typical methods.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10320380","citationCount":"0","resultStr":"{\"title\":\"A Total-Body Ultralow-Dose PET Reconstruction Method via Image Space Shuffle U-Net and Body Sampling\",\"authors\":\"Gaoyu Chen;Sheng Liu;Wenxiang Ding;Li Lv;Chen Zhao;Fenghua Weng;Yong Long;Yunlong Zan;Qiu Huang\",\"doi\":\"10.1109/TRPMS.2023.3333839\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Low-dose positron emission tomography (PET) reconstruction algorithms manage to reduce the injected dose and/or scanning time in PET examination while maintaining the image quality, and thus has been extensively studied. In this article, we proposed a novel ultralow-dose reconstruction method for total-body PET. Specifically, we developed a deep learning model named ISS-Unet based on U-Net and introduced 3-D PixelUnshuffle/PixelShuffle pair in image space to reduce the training time and GPU memory. We then introduced two body sampling methods in the training patch preparation step to improve the training efficiency and local metrics. We also reported the misalignment artifacts that were often neglected in 2-D training. The proposed method was evaluated on the MICCAI 2022 Ultralow-Dose PET Imaging Challenge dataset and won the first prize in the first-round competition according to the comprehensive score combining global and local metrics. In this article, we disclosed the implementation details of the proposed method followed by the comparison results with three typical methods.\",\"PeriodicalId\":46807,\"journal\":{\"name\":\"IEEE Transactions on Radiation and Plasma Medical Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2023-11-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10320380\",\"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/10320380/\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Radiation and Plasma Medical Sciences","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10320380/","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
摘要
低剂量正电子发射计算机断层扫描(PET)重建算法能够在保证图像质量的前提下减少 PET 检查的注射剂量和/或扫描时间,因此被广泛研究。在本文中,我们提出了一种用于全身正电子发射断层扫描的新型超低剂量重建方法。具体来说,我们在 U-Net 的基础上开发了一种名为 ISS-Unet 的深度学习模型,并在图像空间中引入了三维 PixelUnshuffle/PixelShuffle 对,以减少训练时间和 GPU 内存。然后,我们在训练补丁准备步骤中引入了两种体采样方法,以提高训练效率和局部指标。我们还报告了在二维训练中经常被忽略的错位伪影。所提出的方法在 MICCAI 2022 超低剂量 PET 成像挑战赛数据集上进行了评估,并根据全局和局部指标相结合的综合得分在第一轮比赛中获得了一等奖。本文披露了所提方法的实现细节,以及与三种典型方法的比较结果。
A Total-Body Ultralow-Dose PET Reconstruction Method via Image Space Shuffle U-Net and Body Sampling
Low-dose positron emission tomography (PET) reconstruction algorithms manage to reduce the injected dose and/or scanning time in PET examination while maintaining the image quality, and thus has been extensively studied. In this article, we proposed a novel ultralow-dose reconstruction method for total-body PET. Specifically, we developed a deep learning model named ISS-Unet based on U-Net and introduced 3-D PixelUnshuffle/PixelShuffle pair in image space to reduce the training time and GPU memory. We then introduced two body sampling methods in the training patch preparation step to improve the training efficiency and local metrics. We also reported the misalignment artifacts that were often neglected in 2-D training. The proposed method was evaluated on the MICCAI 2022 Ultralow-Dose PET Imaging Challenge dataset and won the first prize in the first-round competition according to the comprehensive score combining global and local metrics. In this article, we disclosed the implementation details of the proposed method followed by the comparison results with three typical methods.