通过逐步优化和深度学习来实现大深度超紧显微镜.
Yuanlong Zhang1,2,3,4, Xiaofei Song5, Jiachen Xie1,2,3,4
1Department of Automation, Tsinghua University, 100084, Beijing, China.
Nature communications
|July 11, 2023
概括
我们开发了一种小型化的集成显微镜,其性能超过商业模型. 这个紧的,与手机兼容的设备利用先进的光学和深度学习来增强成像和便携式诊断.
科学领域:
- 光学和光子学 在光学和光子学.
- 生物医学工程 生物医学工程
- 计算成像技术的成像
背景情况:
- 传统的光学显微镜大,昂贵,性能有限.
- 高性能光学系统的小型化是成像技术的一个重大挑战.
研究的目的:
- 开发一种高度集成的显微镜,具有卓越的光学性能和显著减少的形状因子.
- 为了证明用于便携式诊断应用的紧显微镜的可行性.
主要方法:
- 一个渐进式优化管道被用来系统地优化非球形镜头和衍射光学元件.
- 一个模拟监督深度神经网络被设计用于在光学设计过程中空间变化的解卷.
- 集成光学与计算光学和深度学习技术相结合.
主要成果:
- 获得的光学性能超过了商业显微镜,在0.15cm3和0.5g的包装中具有5×,NA 0.1的目标.
- 与传统显微镜相比,该显微镜的景深提高了10倍以上.
- 展示了成功地集成到手机中,以进行各种样本的便携式诊断.
结论:
- 拟议的方法为设计小型化,高性能成像系统提供了一个新的框架.
- 集成显微镜可以在便携式,可访问的格式中实现先进的光学功能.
- 这项技术在远程诊断和临床诊断中具有潜在的应用.
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