增强模型代算法与图形神经网络用于扩散光学断层扫描
Huangjian Yi1,2, Ruigang Yang1,2, Yishuo Wang1,2
1School of Information Sciences and Technology, Northwest University, Xi'an, Shaanxi 710069, China.
Biomedical optics express
|March 18, 2024
概括
这项研究引入了一个与Levenberg-Marquardt (GATLM) 结合的图形注意力神经网络,用于扩散光学断层扫描 (DOT). GATLM提高了吸收系数重建的准确性,特别是在现实场景中.
科学领域:
- 生物医学光学 生物医学光学
- 医疗成像医学成像
- 计算科学 计算科学
背景情况:
- 扩散光学断层扫描 (DOT) 使用近红外光对生物组织进行成像.
- 由于光子散射和有限的数据,DOT重建具有挑战性,需要提高准确性和计算效率.
- 莱文伯格-马奎特 (LM) 算法是常见的,但计算密集型,可能缺乏精度.
研究的目的:
- 开发一种基于神经网络的代算法,用于增强DOT重建.
- 评估与LM集成的图形卷积神经网络 (GCN) 和图形注意力神经网络 (GAT) 的性能.
- 评估用于准确的光学参数检索的拟议方法的概括能力.
主要方法:
- 建议使用图形神经网络与莱文伯格-马奎特 (GNNLM) 算法相结合.
- 图形数据结构代表了DOT重建的有限元素网格.
- 两种GNN变体,GCNLM和GATLM,通过模拟和幻影研究进行了实验性评估.
主要成果:
- 在模拟中的训练数据分布中,GCNLM表现最好.
- 在训练数据分发之外,GATLM在真实幻影实验中表现出卓越的性能.
- 在没有转移学习的情况下,GATLM展示了从模拟到现实世界的数据的优秀泛化.
结论:
- GATLM提供了一种强大而准确的扩散光学断层扫描重建方法.
- GATLM算法很好地概括,改善了临床环境中的吸收系数分布精度.
- 这种方法对更精确的非侵入性组织光学性质测量具有前景.
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