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Macroscopic fluorescence lifetime topography enhanced via spatial frequency domain imaging

机译:宏观荧光寿命通过空间频域成像增强

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摘要

We report on a macroscopic fluorescence lifetime imaging (MFLI) topography computational framework based around machine learning with the main goal of retrieving the depth of fluorescent inclusions deeply seated in bio-tissues. This approach leverages the depth-resolved information inherent to time-resolved fluorescence data sets coupled with the retrieval of in situ optical properties as obtained via spatial frequency domain imaging (SFDI). Specifically, a Siamese network architecture is proposed with optical properties (OPs) and time-resolved fluorescence decays as input followed by simultaneous retrieval of lifetime maps and depth profiles. We validate our approach using comprehensive in silico data sets as well as with a phantom experiment. Overall, our results demonstrate that our approach can retrieve the depth of fluorescence inclusions, especially when coupled with optical properties estimation, with high accuracy. We expect the presented computational approach to find great utility in applications such as optical-guided surgery. (C) 2020 Optical Society of America
机译:我们报告了基于机器学习的宏观荧光寿命成像(MFLI)地形计算框架,其主要目的是检索在生物组织中深深地坐在生物组织中的荧光夹杂物的深度。该方法利用与通过空间频域成像(SFDI)获得的原位光学性质的检索耦合的时间分辨荧光数据集固有的深度解析信息。具体地,用光学性质(OPS)和作为输入的时间分辨荧光衰减提出了暹罗网络架构,其次是同时检索寿命映射和深度轮廓。我们验证了我们在Silico数据集中的全面验证了我们的方法以及幻影实验。总的来说,我们的结果表明,我们的方法可以检索荧光夹杂物的深度,特别是当与光学特性估计相结合,具有高精度。我们预计所提出的计算方法可以在光学引导手术等应用中找到良好的效用。 (c)2020美国光学学会

著录项

  • 来源
    《Optics Letters》 |2020年第15期|共4页
  • 作者单位

    Rensselaer Polytech Inst Dept Biomed Engn Troy NY 12180 USA;

    Univ Strasbourg ICube Lab 300 Blvd Sebastien Brant F-67412 Illkirch Graffenstaden France;

    Univ Strasbourg ICube Lab 300 Blvd Sebastien Brant F-67412 Illkirch Graffenstaden France;

    Rensselaer Polytech Inst Dept Biomed Engn Troy NY 12180 USA;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 计量学;光学;
  • 关键词

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