Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan.
J Microsc. 2011 Jun;242(3):311-24. doi: 10.1111/j.1365-2818.2010.03473.x. Epub 2010 Dec 9.
By means of multiphoton laser scanning microscopy, neuroscientists can look inside the brain deeper than has ever been possible before. Multiphoton fluorescent images, as all optical images, suffer from degradation caused by a variety of sources (e.g. light dispersion and absorption in the tissue, laser fluctuations, spurious photodetection and staining deficiency). From a modelling perspective, such degradations can be considered the sum of stochastic noise and a background signal. Among the methods proposed in the literature to perform image deconvolution in either confocal or multiphoton fluorescent microscopy, Vicidomini et al. (2009) were the first to incorporate models for noise (a Poisson process) and background signal (spatially constant) in the context of regularized inverse problems. Unfortunately, the so-called split-gradient deconvolution method (SGM) they used did not consider possible spatial variations in the background signal. In this paper, we extend the SGM by adding a maximum-likelihood estimation step for the determination of a spatially varying background signal. We demonstrate that the assumption of a constant background is not always valid in multiphoton laser microscopy and by using synthetic and actual multiphoton fluorescent images, we evaluate the face of validity of the proposed method, and compare its accuracy with the previously introduced SGM algorithm.
通过多光子激光扫描显微镜，神经科学家可以深入观察大脑，这是以前从未有过的。多光子荧光图像与所有光学图像一样，都会受到各种来源的退化影响（例如组织中的光散射和吸收、激光波动、假光探测和染色不足）。从建模的角度来看，这种退化可以被认为是随机噪声和背景信号的总和。在文献中提出的用于在共焦或多光子荧光显微镜中进行图像反卷积的方法中，Vicidomini 等人（2009 年）是第一个在正则化反问题的背景下将噪声模型（泊松过程）和背景信号（空间恒定）纳入其中的人。不幸的是，他们使用的所谓的分裂梯度反卷积方法（SGM）没有考虑背景信号可能存在的空间变化。在本文中，我们通过添加用于确定空间变化背景信号的最大似然估计步骤来扩展 SGM。我们证明了在多光子激光显微镜中，假设背景是恒定的并不总是有效的，并且通过使用合成和实际的多光子荧光图像，我们评估了所提出的方法的有效性，并将其准确性与之前介绍的 SGM 算法进行了比较。
