基于深度学习的技术,用于从低剂量扫描中估计高质量的全剂量正子辐射断层扫描图像:系统审查
Negisa Seyyedi1, Ali Ghafari2, Navisa Seyyedi3
1Nursing and Midwifery Care Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran.
BMC medical imaging
|September 11, 2024
概括
深度学习显示了提高低剂量正子辐射断层扫描 (PET) 图像到全剂量质量的前景. 这种技术可以通过减少辐射剂量来产生高质量的PET扫描来改善临床实践.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 低剂量正子发射断层扫描 (PET) 图像是可取的,以减少辐射暴露.
- 从低剂量数据中合成高质量的PET图像仍然是一个挑战.
研究的目的:
- 系统地审查深度学习算法的潜力,以将低剂量PET图像转换为全剂量质量.
- 评估各种深度学习模型在不同身体区域的有效性.
主要方法:
- 在PubMed,科学网络,Scopus和IEEE数据库 (2017-2023) 的系统文献搜索.
- 包括55篇使用深度学习模型的文章,如生成对抗网络 (GAN) 和UNET.
- 分析各种数据集,预处理技术,输入数据和丢失函数.
主要成果:
- 深度学习模型展示了从低剂量输入中合成高质量的PET图像的巨大潜力.
- 定量和定性评估,包括医生评估,支持这些方法的有效性.
- 审查的研究表明,在各种身体区域中成功应用.
结论:
- 深度学习算法为从低剂量采集中生成高质量的PET图像提供了一个有希望的方法.
- 这项技术有可能提高PET成像中的临床效用和患者安全.
相关概念视频
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