Deconvolution of Spherical Aberration in Confocal Microscopy of Thick Tissues

描述(申请人提供)：共焦荧光显微镜可直接消除从散焦内部结构发出的光。这使得能够观察到厚样品中深部的荧光团标记的过程或3D结构。不幸的是，高性能物镜是为油、甘油或水等浸没液体设计的；样品的折射率必须与这些选项紧密匹配。校正环可以校正特定深度的球面像差；然而，使用流行的自动3D光学切片技术进行调整是不现实的。对厚厚的“体内”组织或活细胞培养的研究质量受到了影响；这在神经生物学领域尤其严重，因为组织内的神经结构很大。折射率失配只会在盖子玻璃下几微米处引起球面像差，当聚焦更深入样本时，球差会急剧增加；对于神经生物学来说，球差会增加数百微米。这种像差对荧光图像(单光子或多光子)有几种严重的损害：随着焦面深入样品，图像强度急剧减弱，在轴向引起显著的几何失真，分辨率(特别是轴向)显著降低。荧光团分布的观测深度和量化值有限。反卷积是一种被广泛使用的软件算法，它可以将光学系统的影响反转。它重新计算未模糊的图像，并使用点扩散函数(PSF)产生定量有效的荧光团强度分布。PSF是从次分辨荧光珠的图像中推导出来的，或者是使用“盲”算法从图像本身的特征中推导出来的。AutoQuant是用于生物医学广场和共聚焦荧光显微镜的去卷积软件的领先供应商。现有的反卷积算法背后的基本理论和原理需要一个恒定的PSF。现有的反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反卷积方法，该方法绕过了PSF通常的空间不变性要求。这些新算法的发展将恢复3D共聚焦荧光显微镜对厚样品的分辨率、几何完整性和强度有效性。我们希望建议的算法足够实用，可以作为我们现有的软件反卷积和可视化产品的附加组件。我们将在厚组织中神经结构的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.