Deconvolution of Spherical Aberration in Confocal Microscopy of Thick Tissues

描述（由申请人提供）：共聚焦荧光显微镜直接消除从失焦内部结构发出的光。这使得能够观察厚标本深处的荧光团标记过程或3D结构。不幸的是，高性能物镜是为油、甘油或水等浸液设计的;样品的折射率必须与这些选项密切匹配。校正环可以校正特定深度处的球面像差;然而，使用流行的自动3D光学切片技术进行调整是不实际的。对厚的“体内”组织或活细胞培养物的研究质量受到损害;这在神经生物学中尤其严重，因为组织内的神经结构很大。折射率不匹配在盖玻片下方仅几微米处引起球面像差，并且在聚焦到样本更深处时急剧增加;对于神经生物学而言为数百微米。这种像差以几种严重的方式损害荧光图像（1或多光子）：随着焦平面深入样品，图像强度急剧减小，在轴向方向上引起显著的几何失真，并且，分辨率（特别是轴向）大大降低。观察的深度和荧光团分布的定量值是有限的。反卷积是一种广泛使用的软件算法，它可以“反转”光学系统的影响。它重新计算未模糊的图像，并使用点扩散函数或PSF产生定量有效的荧光团强度分布。PSF是从亚分辨荧光珠的图像中导出的，或者使用“盲”算法从图像本身的特征中推导出。AutoQuant是用于生物医学宽视场和共聚焦荧光显微镜的去卷积软件的领先供应商。现有反卷积算法背后的基本理论和原理需要恒定的PSF。现有的去卷积技术无法完全恢复具有深度相关球差的图像。我们提出了一种新的方法去卷积，绕过通常的空间不变性要求的PSF。这些新算法的发展将恢复的分辨率，几何完整性和强度的有效性，厚标本的三维共聚焦荧光显微镜。我们期望所提出的算法将是足够实用的，作为一个附加到我们现有的软件去卷积和可视化产品。我们将在厚组织中的神经结构图像上测试这项技术。

DESCRIPTION (provided by applicant): The confocal fluorescent microscope directly eliminates the light emitted from out-of-focus internal structures. This has enabled the observation of fluorophore-labeled processes or 3D structures deep within thick specimens. Unfortunately, high performance objective lenses are designed for immersion fluids, such as oil, glycerol, or water; the index of the specimen must match these options closely. Correction collars can correct for spherical aberration at specific depths; however the adjustments are not practical using the popular technique of automated 3D optical sectioning. The quality of the research on thick "in-vivo" tissues, or living cellular cultures is compromised; it is especially severe in neurobiology because neural structures within tissues are large. The refractive index mismatch induces spherical aberration just a few microns beneath the cover-glass, and increases dramatically upon focusing deeper into the specimen; hundreds of microns for neurobiology. This aberration impairs the fluorescence images (1 or multiple-photon) in several serious ways: As the focal plane extends deeper into specimen, the image intensity diminishes dramatically, significant geometric distortions are induced in the axial direction, and, the resolution (especially axial) is strongly reduced. The depth of observation and the quantitative value of the fluorophore distribution are limited. Deconvolution is a widely-used, software algorithm that "inverts" the impact of the optical system. It recalculates the un-blurred image and produces a quantitatively valid fluorophore intensity distribution using the point spread function, or PSF. The PSF is derived from images of sub-resolved fluorescent beads, or deduced from the characteristics of the image itself using the "blind" algorithm. AutoQuant is a leading supplier of deconvolution software used for biomedical wide-field and confocal fluorescent microscopy. The basic theory and principles behind existing deconvolution algorithms require a constant PSF. Existing deconvolution technology is unable to fully restore images with depth-dependent spherical aberration. We propose a new approach to deconvolution that bypasses the usual spatial invariance requirement for the PSF. The development of these new algorithms will restore the resolution, geometric integrity, and intensity validity to 3D confocal fluorescent microscopy for thick specimens. We expect the proposed algorithms will be sufficiently practical to be incorporated as an add-on to our existing software deconvolution and visualization products. We will test this technology on images of neural structures in thick tissues.