基于物理模型受约束的神经网络的三维光学成像的高度强大的重建框架
Xueli Chen1,2,3, Yu Meng1,2, Lin Wang4
1Center for Biomedical-photonics and Molecular Imaging, Advanced Diagnostic-Therapy Technology and Equipment Key Laboratory of Higher Education Institutions in Shaanxi Province, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, People's Republic of China.
Physics in medicine and biology
|February 23, 2024
概括
这项研究引入了一种新的物理模型受约束的神经网络,用于3D光学成像重建. 该框架在不需要培训数据的情况下实现了高精度和稳定性,克服了传统和深度学习方法的局限性.
科学领域:
- 生物医学光学 生物医学光学
- 计算成像技术的成像
- 机器学习用于科学
背景情况:
- 从表面测量进行三维光学成像重建是一个不合时宜的问题.
- 传统方法严重依赖于事先的信息,限制了稳定性和适应性.
- 现有的深度学习方法需要大量的训练数据,导致不良的概括和长时间的获取时间.
研究的目的:
- 为三维光学成像开发一个高度强大的重建框架.
- 克服传统的基于规范化和数据驱动的深度学习方法的局限性.
- 为了提高光学成像重建的准确性,稳定性和通用性.
主要方法:
- 提出了一个物理模型受约束的神经网络框架.
- 神经网络从表面测量产生目标分布.
- 一个物理模型计算了表面光分布,用平均平方误差作为优化损失函数.
- 采用了可移动区域战略,以减少对事先信息的依赖.
主要成果:
- 该框架在模拟和体内实验中表现出高精度,稳定性和多功能性.
- 对不同的目标分布,噪声和深度变化而言,性能强大.
- 实现了高空间分辨率.
结论:
- 拟议的框架为3D光学成像重建提供了一个强大的和可通用的解决方案.
- 它消除了手动参数调整和训练数据集的需要.
- 这种方法为光学成像重建提供了新的视角,节省了时间和资源.
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