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基于多角度采集和图像融合重建的各向同性分辨率三维明场显微镜。

Three-dimensional bright-field microscopy with isotropic resolution based on multi-view acquisition and image fusion reconstruction.

机构信息

Dipartimento di Fisica, Politecnico di Milano, piazza Leonardo da Vinci 32, 20133, Milan, Italy.

Istituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle ricerche, piazza Leonardo da Vinci 32, 20133, Milan, Italy.

出版信息

Sci Rep. 2020 Jul 29;10(1):12771. doi: 10.1038/s41598-020-69730-4.

Abstract

Optical Projection Tomography (OPT) is a powerful three-dimensional imaging technique used for the observation of millimeter-scaled biological samples, compatible with bright-field and fluorescence contrast. OPT is affected by spatially variant artifacts caused by the fact that light diffraction is not taken into account by the straight-light propagation models used for reconstruction. These artifacts hinder high-resolution imaging with OPT. In this work we show that, by using a multiview imaging approach, a 3D reconstruction of the bright-field contrast can be obtained without the diffraction artifacts typical of OPT, drastically reducing the amount of acquired data, compared to previously reported approaches. The method, purely based on bright-field contrast of the unstained sample, provides a comprehensive picture of the sample anatomy, as demonstrated in vivo on Arabidopsis thaliana and zebrafish embryos. Furthermore, this bright-field reconstruction can be implemented on practically any multi-view light-sheet fluorescence microscope without complex hardware modifications or calibrations, complementing the fluorescence information with tissue anatomy.

摘要

光学投影层析成像(OPT)是一种强大的三维成像技术,用于观察毫米级生物样本,兼容明场和荧光对比。OPT 受到空间变异性伪影的影响,这些伪影是由重建中使用的直光传播模型未考虑光衍射引起的。这些伪影阻碍了 OPT 的高分辨率成像。在这项工作中,我们表明,通过使用多视图成像方法,可以获得无 OPT 典型衍射伪影的明场对比的 3D 重建,与以前报道的方法相比,大大减少了采集的数据量。该方法完全基于未染色样本的明场对比,提供了样本解剖结构的全面描述,在活体拟南芥和斑马鱼胚胎上得到了验证。此外,这种明场重建可以在几乎任何多视角光片荧光显微镜上实现,而无需复杂的硬件修改或校准,用组织解剖学补充荧光信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e5e/7392767/a6359e442435/41598_2020_69730_Fig1_HTML.jpg

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