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相关概念视频

Propagation of Uncertainty from Random Error00:59

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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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.

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|October 3, 2024
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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.

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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.