深度学习加速基于代模型的光流量校正用于光声断层扫描
Zhaoyong Liang1,2,3, Shuangyang Zhang1,2,3, Zhichao Liang1,2,3
1School of Biomedical Engineering, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, China.
Photoacoustics
|March 22, 2024
概括
里叶神经运算器 (FNO) 通过有效地纠正光流量 (LF) 变化来加速光声断层扫描 (PAT). 这种新的方法显著减少了用于精确的染色体成像在生物组织的计算时间.
科学领域:
- 生物医学成像技术 生物医学成像技术
- 计算成像技术的成像
- 光学物理学的光学物理学
背景情况:
- 光声断层扫描 (PAT) 可视化了组织中的染色体分布.
- 光流量 (LF) 的变化损害了PAT的准确性和量化.
- 目前的LF校正方法是计算密集的.
研究的目的:
- 在光声断层扫描 (PAT) 中开发一种有效的光流率 (LF) 校正方法.
- 为了加快PAT成像中的代校正程序.
- 为了保持高质量的LF校正,同时减少计算负载.
主要方法:
- 使用福里埃神经运算符 (FNO),用于解决部分微分方程的神经网络.
- 在基于有限元素的光传输模拟数据上训练了FNO模型.
- 将受过训练的FNO模型集成到代校正过程中,取代传统的LF估计器.
主要成果:
- 基于FNO的方法实现了与传统代方法相匹配的LF校正质量.
- 与传统方法相比,校正时间缩短了30倍以上.
- 通过模拟和实验结果证明了有效性.
结论:
- 在PAT中,FNO为LF校正提供了显著加速和高效的解决方案.
- 这种方法提高了定量PAT成像的实用性.
- 这种方法有望提高生物医学成像技术的速度和准确性.
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