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基于图案化光照明和探测的时分辨散光学层析成像。

Time-resolved diffuse optical tomography with patterned-light illumination and detection.

机构信息

Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180, USA.

出版信息

Opt Lett. 2010 Jul 1;35(13):2121-3. doi: 10.1364/OL.35.002121.

DOI:10.1364/OL.35.002121
PMID:20596166
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4638228/
Abstract

This investigation explores the feasibility of performing diffuse optical tomography based on time-domain wide-field illumination and detection strategies. Wide-field patterned excitation and detection schemes are investigated in transmittance geometry with time-gated detection channels. A Monte Carlo forward model is employed to compute the time-resolved Jacobians for rigorous light propagation modeling. We demonstrate both in silico and experimentally that reconstructions of absorption structures based on wide-field patterned-light strategies are feasible and outperform classical point excitation schemes for similar data set sizes. Moreover, we demonstrate that time-domain information is retained even though large spatial areas are illuminated. The enhanced time-domain data set allows for quantitative three-dimensional imaging in thick tissue based on relatively small data sets associated with much shorter acquisition times.

摘要

本研究探索了基于时域宽场照明和探测策略进行漫射光学层析成像的可行性。在透射几何中采用时门控探测通道研究了宽场图案化激发和探测方案。采用蒙特卡罗正演模型计算用于严格光传播建模的时分辨雅可比矩阵。我们通过数值模拟和实验证明,基于宽场图案化光策略的吸收结构重建是可行的,并且对于类似的数据集大小,优于经典的点激发方案。此外,我们证明即使大面积照明,也能保留时域信息。增强的时域数据集允许基于与较短采集时间相关联的相对较小数据集在厚组织中进行定量三维成像。

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Deep learning-based fusion of widefield diffuse optical tomography and micro-CT structural priors for accurate 3D reconstructions.基于深度学习的宽场漫射光学层析成像与微观计算机断层扫描结构先验信息融合,用于精确三维重建。
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