4.6 Article

Optimizing illumination in three-dimensional deconvolution microscopy for accurate refractive index tomography

期刊

OPTICS EXPRESS
卷 29, 期 5, 页码 6293-6301

出版社

OPTICAL SOC AMER
DOI: 10.1364/OE.412510

关键词

-

类别

资金

  1. Tomocube
  2. KAIST (Up program)
  3. National Research Foundation of Korea [2015R1A3A2066550, 2017M3C1A3013923, 2018K000396]

向作者/读者索取更多资源

In light transmission microscopy, axial scanning does not directly provide tomographic reconstruction of specimen. Phase deconvolution microscopy can convert a raw intensity image stack into a refractive index tomogram, the intrinsic sample contrast which can be exploited for quantitative morphological analysis. However, this technique is limited by reconstruction artifacts due to unoptimized optical conditions, which leads to a sparse and non-uniform optical transfer function. Here, we propose an optimization method based on simulated annealing to systematically obtain optimal illumination schemes that enable artifact-free deconvolution. The proposed method showed precise tomographic reconstruction of unlabeled biological samples.
In light transmission microscopy, axial scanning does not directly provide tomographic reconstruction of specimen. Phase deconvolution microscopy can convert a raw intensity image stack into a refractive index tomogram, the intrinsic sample contrast which can be exploited for quantitative morphological analysis. However, this technique is limited by reconstruction artifacts due to unoptimized optical conditions, which leads to a sparse and non-uniform optical transfer function. Here, we propose an optimization method based on simulated annealing to systematically obtain optimal illumination schemes that enable artifact-free deconvolution. The proposed method showed precise tomographic reconstruction of unlabeled biological samples. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据