关于使用神经网络方法进行低剂量排放断层扫描后重建Denoising的审查
Alexandre Bousse1, Venkata Sai Sundar Kandarpa1, Kuangyu Shi2
1Univ. Brest, LATIM, INSERM UMR 1101, 29238 Brest, France.
IEEE transactions on radiation and plasma medical sciences
|October 21, 2024
概括
低剂量排放断层扫描 (ET) 成像面临着噪声挑战. 本综述强调深度神经网络 (NN) 是改善低剂量ET图像质量和分辨率的有希望的解决方案.
科学领域:
- 医疗成像医学成像
- 核医学就是核医学.
- 计算成像技术的成像
背景情况:
- 低剂量发射断层扫描 (ET) 对功能医学成像至关重要,但光子计数随机性会放大噪声.
- 低剂量ET的图像质量下降妨碍了准确的诊断和研究.
研究的目的:
- 审查目前低剂量ET的后处理技术.
- 强调深度神经网络 (NN) 在降噪和图像增强方面的作用.
- 探索基于NN的低剂量ET的未来进展.
主要方法:
- 对低剂量ET的后处理方法的文献综述.
- 专注于深度学习和神经网络 (NN) 应用.
- 对基于 NN 的 ET 的当前研究和未来趋势的分析.
主要成果:
- 深度神经网络显示出在低剂量ET中减轻噪声的巨大潜力.
- 无线网络可以提高图像质量和分辨率,提高诊断准确度.
- 目前正在探索各种NN架构以获得最佳性能.
结论:
- 深度学习提供了一种强大的方法来克服低剂量ET的噪音限制.
- 基于NN的技术的进步有望显著提高医学成像能力.
- 对NN架构和培训策略的进一步研究对于未来的发展至关重要.
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