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使用去卷积的线扫描彩色共聚焦显微镜对皮肤组织进行多深度成像

Line-scan chromatic confocal microscopy for multi-depth imaging of skin tissue using deconvolution.

作者信息

Zhao Wenhao, Liang YongKang, Shang Rui, Yue Kun, Guo Wenping, Xia Min

机构信息

School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.

出版信息

Biomed Opt Express. 2025 May 29;16(6):2516-2527. doi: 10.1364/BOE.562118. eCollection 2025 Jun 1.

Abstract

In this paper, we present a line-scan chromatic confocal microscopy for rapid, multi-depth imaging of non-transparent skin tissue. Leveraging spectral dispersion encoding, this system enables the simultaneous acquisition of reflectance data across a depth range exceeding 180 m in skin tissue, effectively eliminating mechanical axial scanning and minimizing associated artifacts. With a lateral effective field of view (FOV) of 13.2 mm, the system provides a wide-field perspective for comprehensive tissue analysis. Furthermore, to overcome the typical limitations of lateral resolution in line-scan systems, we employ pre-calibrated deconvolution using an empirically determined point spread function obtained with a micro-spot mirror. We demonstrate deep-tissue tomography with bovine skin tissue, achieving an increase in resolution from 45.25 lp/mm to 50.79 lp/mm post-deconvolution, as quantified using a USAF 1951 target. These results highlight the potential of line-scan chromatic confocal microscopy with pre-calibrated deconvolution as a powerful platform for multi-depth biomedical imaging applications requiring enhanced lateral resolution and contrast.

摘要

在本文中,我们展示了一种用于对不透明皮肤组织进行快速、多深度成像的线扫描彩色共聚焦显微镜。该系统利用光谱色散编码,能够在皮肤组织中同时获取超过180μm深度范围内的反射率数据,有效消除了机械轴向扫描并将相关伪像降至最低。该系统横向有效视场(FOV)为13.2mm,为全面的组织分析提供了宽视野视角。此外,为了克服线扫描系统中横向分辨率的典型限制,我们使用通过微点镜获得的经验确定的点扩散函数进行预校准去卷积。我们用牛皮肤组织展示了深度组织断层扫描,使用美国空军1951靶标进行量化,去卷积后分辨率从45.25 lp/mm提高到50.79 lp/mm。这些结果突出了具有预校准去卷积的线扫描彩色共聚焦显微镜作为一个强大平台的潜力,可用于需要提高横向分辨率和对比度的多深度生物医学成像应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b399/12265477/18b8b9e322db/boe-16-6-2516-g001.jpg

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