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Francesco Fersini^1,2,3, Alessandro Zunino¹, Pietro Morerio⁴

¹Molecular Microscopy and Spectroscopy, Istituto Italiano di Tecnologia, Genoa, Italy.

Biomedical optics express

|March 2, 2026

概括

我们为激光扫描显微镜 (LSM) 开发了一种新的自适应光学 (AO) 方法,该方法使用图像扫描显微镜和神经网络来估计光学偏差. 这使得更快,更少的侵入性,高分辨率的成像,没有专门的硬件.

科学领域:

光学成像技术的使用.
显微镜的使用方法
生物物理学的生物物理.

背景情况:

激光扫描显微镜 (LSM) 对生命科学至关重要,但受到光学偏差的限制.
适应光学 (AO) 可以纠正异常,但通常需要侵入性硬件或长时间的样品暴露.
目前的偏差估计方法是样本侵入性和不那么用户友好.

研究的目的:

为共聚焦激光扫描显微镜 (CLSM) 提出一个简单,高效的自适应光学 (AO) 策略.
为了证明图像扫描显微镜数据集内在包含异常信息.
为了实现更快,更少的侵入性,更容易获得的高分辨率成像.

主要方法:

使用图像扫描显微镜与探测器阵列和适应光学 (AO) 元素用于光束成型.
开发了一个定制的卷积神经网络 (CNN) 来解码偏差信息.
从单次获取数据集中估计到第11个泽尼克系数的误差.

主要成果:

证明了检测器阵列数据集固有的编码异常信息.
从单个CLSM收购中使用CNN成功解码了异常.
在LSM中建立了异常传感的新范式.

结论:

拟议的方法可以更快,更少的侵入性估计LSM的偏差.
这种数据驱动的方法为先进的异常解码策略开辟了道路.
该策略与传统的AO方法相协同,提高了高分辨率成像的可访问性.

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