自主电子断层扫描重建与机器学习
William Millsaps1, Jonathan Schwartz2, Zichao Wendy Di3
1Department of Nuclear Engineering & Radiological Sciences, University of Michigan, 2300 Hayward St, Ann Arbor, MI 48109, USA.
概括
电子断层扫描中的压缩传感 (CS) 提高了分辨率,但需要仔细调整参数. 本研究介绍了贝叶斯优化与基于动量的CS来自动选择参数,显著减少计算时间和提高3D重建质量.
科学领域:
- 材料科学 材料科学 材料科学
- 影像科学 影像科学
- 计算科学 计算科学
背景情况:
- 现代电子断层扫描通过使用压缩传感 (CS) 方法,在较低剂量下实现更高分辨率.
- 在CS断层扫描中,以稀疏度强调的重建算法依赖于可调节的参数,这些参数对重建质量产生重大影响.
研究的目的:
- 调查CS断层扫描中可调节参数对重建质量的影响.
- 开发一种自动化方法,用于CS断层扫描中高效的参数选择.
- 为了减少电子断层扫描中3D重建的计算时间.
主要方法:
- 帕雷托前线分析以确定最佳的参数权重,以最大限度地减少总变化 (TV).
- 将动量纳入梯度下降算法,以减轻CS重建中的过度平滑.
- 贝叶斯优化与高斯过程的应用,用于自动断层扫描参数选择.
主要成果:
- 重量加重的电视最小化可重复地产生高质量的断层图像.
- 过度的电视最小化导致过度平滑的三维 (3D) 重建.
- 基于动量的CS和贝叶斯优化将SrTiO3纳米立方体重建的计算时间减少了80%.
- 自动参数选择对于大规模的断层学模拟至关重要.
结论:
- 基于动量的压缩传感断层扫描,结合贝叶斯优化,为3D材料表征提供了一种高效和强大的方法.
- 自动参数选择对于推进电子断层扫描对各种材料的3D表征能力至关重要.
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