从低剂量扫描中估算高质量全剂量正电子发射断层扫描图像的深度学习技术:系统性综述

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING BMC Medical Imaging Pub Date : 2024-09-11 DOI:10.1186/s12880-024-01417-y
Negisa Seyyedi, Ali Ghafari, Navisa Seyyedi, Peyman Sheikhzadeh
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引用次数: 0

摘要

本系统综述旨在评估深度学习算法将不同身体区域的低剂量正电子发射断层扫描(PET)图像转换为全剂量 PET 图像的潜力。本综述通过搜索 PubMed、Web of Science、Scopus 和 IEEE 数据库,共收录了 55 篇发表于 2017 年至 2023 年间的文章,这些文章利用生成式对抗网络和 UNET 等各种深度学习模型来合成高质量 PET 图像。这些研究涉及不同的数据集、图像预处理技术、输入数据类型和损失函数。使用定量和定性方法对生成的 PET 图像进行了评估,包括医生评估和各种去噪技术。综述结果表明,深度学习算法在从低剂量正电子发射计算机断层图像生成高质量正电子发射计算机断层图像方面具有广阔的前景,可在临床实践中发挥作用。
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Deep learning-based techniques for estimating high-quality full-dose positron emission tomography images from low-dose scans: a systematic review
This systematic review aimed to evaluate the potential of deep learning algorithms for converting low-dose Positron Emission Tomography (PET) images to full-dose PET images in different body regions. A total of 55 articles published between 2017 and 2023 by searching PubMed, Web of Science, Scopus and IEEE databases were included in this review, which utilized various deep learning models, such as generative adversarial networks and UNET, to synthesize high-quality PET images. The studies involved different datasets, image preprocessing techniques, input data types, and loss functions. The evaluation of the generated PET images was conducted using both quantitative and qualitative methods, including physician evaluations and various denoising techniques. The findings of this review suggest that deep learning algorithms have promising potential in generating high-quality PET images from low-dose PET images, which can be useful in clinical practice.
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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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