{"title":"利用神经网络方法对低剂量发射断层扫描重建后去噪的综述","authors":"Alexandre Bousse;Venkata Sai Sundar Kandarpa;Kuangyu Shi;Kuang Gong;Jae Sung Lee;Chi Liu;Dimitris Visvikis","doi":"10.1109/TRPMS.2023.3349194","DOIUrl":null,"url":null,"abstract":"Low-dose emission tomography (ET) plays a crucial role in medical imaging, enabling the acquisition of functional information for various biological processes while minimizing the patient dose. However, the inherent randomness in the photon counting process is a source of noise which is amplified in low-dose ET. This review article provides an overview of existing post-processing techniques, with an emphasis on deep neural network (NN) approaches. Furthermore, we explore future directions in the field of NN-based low-dose ET. This comprehensive examination sheds light on the potential of deep learning in enhancing the quality and resolution of low-dose ET images, ultimately advancing the field of medical imaging.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 4","pages":"333-347"},"PeriodicalIF":4.6000,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10379513","citationCount":"0","resultStr":"{\"title\":\"A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising With Neural Network Approaches\",\"authors\":\"Alexandre Bousse;Venkata Sai Sundar Kandarpa;Kuangyu Shi;Kuang Gong;Jae Sung Lee;Chi Liu;Dimitris Visvikis\",\"doi\":\"10.1109/TRPMS.2023.3349194\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Low-dose emission tomography (ET) plays a crucial role in medical imaging, enabling the acquisition of functional information for various biological processes while minimizing the patient dose. However, the inherent randomness in the photon counting process is a source of noise which is amplified in low-dose ET. This review article provides an overview of existing post-processing techniques, with an emphasis on deep neural network (NN) approaches. Furthermore, we explore future directions in the field of NN-based low-dose ET. This comprehensive examination sheds light on the potential of deep learning in enhancing the quality and resolution of low-dose ET images, ultimately advancing the field of medical imaging.\",\"PeriodicalId\":46807,\"journal\":{\"name\":\"IEEE Transactions on Radiation and Plasma Medical Sciences\",\"volume\":\"8 4\",\"pages\":\"333-347\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-01-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10379513\",\"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/10379513/\",\"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/10379513/","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
低剂量发射断层扫描(ET)在医学成像中起着至关重要的作用,它能获取各种生物过程的功能信息,同时最大限度地减少病人的剂量。然而,光子计数过程中固有的随机性是噪声的来源之一,而低剂量 ET 会放大这种噪声。这篇综述文章概述了现有的后处理技术,重点介绍了深度神经网络 (NN) 方法。此外,我们还探讨了基于 NN 的低剂量 ET 领域的未来发展方向。这一全面研究揭示了深度学习在提高低剂量 ET 图像质量和分辨率方面的潜力,最终推动医学成像领域的发展。
A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising With Neural Network Approaches
Low-dose emission tomography (ET) plays a crucial role in medical imaging, enabling the acquisition of functional information for various biological processes while minimizing the patient dose. However, the inherent randomness in the photon counting process is a source of noise which is amplified in low-dose ET. This review article provides an overview of existing post-processing techniques, with an emphasis on deep neural network (NN) approaches. Furthermore, we explore future directions in the field of NN-based low-dose ET. This comprehensive examination sheds light on the potential of deep learning in enhancing the quality and resolution of low-dose ET images, ultimately advancing the field of medical imaging.