辐射图像重建和不确定性量化使用高斯过程之前的高斯过程
Jaewon Lee1, Tenzing H Joshi2, Mark S Bandstra2
1Department of Nuclear Engineering, University of California, Berkeley, Berkeley, CA, 94720, USA. jwonlee@berkeley.edu.
Scientific reports
|October 3, 2024
概括
我们引入使用高斯过程先验 (GPP) 的贝叶斯图像重建框架,以提高图像质量和不确定性量化. 这种方法改进了ML-EM,在辐射成像中提供了更好的源分布理解.
科学领域:
- 医疗成像医学成像
- 计算科学 计算科学
- 统计建模 统计建模
背景情况:
- 最大概率预期最大化 (ML-EM) 算法在图像重建方面存在局限性.
- 贝叶斯方法为改善图像质量和不确定性量化提供了潜力.
研究的目的:
- 开发一个全面的贝叶斯框架用于图像重建和不确定性量化.
- 为了克服现有的ML-EM算法的局限性,使用高斯过程先验 (GPP).
主要方法:
- 使用一个零平均值的高斯过程 (GP) 与可选择的共变函数用于先前分布.
- 用实证贝叶斯来自动选择超参数,增强可解释性.
- 整合了多模式成像和数据融合的GP共变性中的结构priors.
- 应用贝叶斯式不确定性量化技术,如预先条件的克兰克-尼科尔森和拉普拉斯近似.
主要成果:
- 与ML-EM相比,GPP框架显著提高了图像质量.
- 通过不确定性量化,对源分布有了更好的理解.
- 通过整合结构先验来显著提高图像质量.
结论:
- 拟议的GPP框架为贝叶斯图像重建提供了一种多功能和有效的方法.
- 它比ML-EM提供了更高的性能,特别是在辐射成像应用中.
- 该方法促进了可靠的不确定性量化,并允许整合先前的结构信息.
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