Department of Histopathology, Northwick Park Hospital, Watford Road, London, HA1 3UJ, England, UK.
J Microsc. 2010 Feb;237(2):192-9. doi: 10.1111/j.1365-2818.2009.03323.x.
This paper addresses the problem of 3D deconvolution of through focus widefield microscope datasets (Z-stacks). One of the most difficult stages in brightfield deconvolution is finding the point spread function. A theoretically calculated point spread function (called a 'synthetic PSF' in this paper) requires foreknowledge of many system parameters and still gives only approximate results. A point spread function measured from a sub-resolution bead suffers from low signal-to-noise ratio, compounded in the brightfield setting (by contrast to fluorescence) by absorptive, refractive and dispersal effects. This paper describes a method of point spread function estimation based on measurements of a Z-stack through a thin sample. This Z-stack is deconvolved by an idealized point spread function derived from the same Z-stack to yield a point spread function of high signal-to-noise ratio that is also inherently tailored to the imaging system. The theory is validated by a practical experiment comparing the non-blind 3D deconvolution of the yeast Saccharomyces cerevisiae with the point spread function generated using the method presented in this paper (called the 'extracted PSF') to a synthetic point spread function. Restoration of both high- and low-contrast brightfield structures is achieved with fewer artefacts using the extracted point spread function obtained with this method. Furthermore the deconvolution progresses further (more iterations are allowed before the error function reaches its nadir) with the extracted point spread function compared to the synthetic point spread function indicating that the extracted point spread function is a better fit to the brightfield deconvolution model than the synthetic point spread function.
本文解决了通过聚焦宽场显微镜数据集（Z 堆栈）进行 3D 反卷积的问题。明场反卷积中最困难的阶段之一是找到点扩散函数。理论上计算的点扩散函数（本文中称为“合成 PSF”）需要预先了解许多系统参数，并且仍然只能得到近似结果。从亚分辨率珠粒测量的点扩散函数由于信噪比低而受到影响，在明场设置（与荧光相比）中，吸收、折射和色散效应会使情况变得更糟。本文描述了一种基于通过薄样品的 Z 堆栈进行点扩散函数估计的方法。该 Z 堆栈通过从同一 Z 堆栈导出的理想化点扩散函数进行反卷积，以产生具有高信噪比的点扩散函数，该函数也固有地适合成像系统。该理论通过一个实际实验得到了验证，该实验比较了使用本文提出的方法（称为“提取的 PSF”）生成的非盲 3D 反卷积与使用合成 PSF 对酵母酿酒酵母进行的非盲 3D 反卷积，结果表明，使用该方法获得的提取的点扩散函数可以实现更高的对比度和更低的对比度的亮场结构的恢复，并且具有更少的伪影。此外，与合成 PSF 相比，使用提取的 PSF 可以进行更多的迭代（在误差函数达到最低点之前），这表明提取的 PSF 比合成 PSF 更适合亮场反卷积模型。
