Politecnico di Milano, Department of Physics, piazza Leonardo da Vinci 32, Milan, Italy.
National Council of Research of Italy, Institute of Photonics and Nanotechnology, Milan, Italy.
Microsc Res Tech. 2022 Jun;85(6):2282-2291. doi: 10.1002/jemt.24085. Epub 2022 Feb 24.
Combining the information coming from multiview acquisitions is a problem of great interest in light-sheet microscopy. Aligning the views and increasing the resolution of their fusion can be challenging, especially if the setup is not fully calibrated. Here, we tackle these issues by proposing a new reconstruction method based on autocorrelation inversion that avoids alignment procedures. On top of this, we add a blind deconvolution step to improve the resolution of the final reconstruction. Our method permits us to achieve inherently aligned, highly resolved reconstructions while, at the same time, estimating the unknown point-spread function of the system. RESEARCH HIGHLIGHTS: We tackle the problem of multiview light-sheet deconvolution with a blind approach of autocorrelation inversion Our method recovers the object and PSF, requires no alignment and calibration, and enhances the reconstruction of the specimen.
在光片显微镜中，结合来自多视角采集的信息是一个非常有趣的问题。对齐视图并提高其融合的分辨率可能具有挑战性，特别是如果设置未完全校准的话。在这里，我们通过提出一种新的基于自相关反演的重建方法来解决这些问题，该方法避免了对齐过程。在此基础上，我们添加了一个盲去卷积步骤来提高最终重建的分辨率。我们的方法允许我们在估计系统未知点扩散函数的同时，实现固有对齐的高分辨率重建。研究亮点：我们使用自相关反演的盲方法解决多视角光片去卷积问题。我们的方法恢复了物体和 PSF，不需要对齐和校准，并且增强了标本的重建。
