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荧光层析智能工具包:模拟、重建与验证。

Smart Toolkit for Fluorescence Tomography: Simulation, Reconstruction, and Validation.

出版信息

IEEE Trans Biomed Eng. 2020 Jan;67(1):16-26. doi: 10.1109/TBME.2019.2907460. Epub 2019 Apr 11.

Abstract

OBJECTIVE

Fluorescence molecular tomography (FMT) can provide valuable molecular information by mapping the bio-distribution of fluorescent reporter molecules in the intact organism. Various prototype FMT systems have been introduced during the past decade. However, none of them has evolved as a standard tool for routine biomedical research. The goal of this paper is to develop a software package that can automate the complete FMT reconstruction procedure.

METHODS

We present smart toolkit for fluorescence tomography (STIFT), a comprehensive platform comprising three major protocols: 1) virtual FMT, i.e., forward modeling and reconstruction of simulated data; 2) control of actual FMT data acquisition; and 3) reconstruction of experimental FMT data.

RESULTS

Both simulation and phantom experiments have shown robust reconstruction results for homogeneous and heterogeneous tissue-mimicking phantoms containing fluorescent inclusions.

CONCLUSION

STIFT can be used for optimization of FMT experiments, in particular for optimizing illumination patterns.

SIGNIFICANCE

This paper facilitates FMT experiments by bridging the gaps between simulation, actual experiments, and data reconstruction.

摘要

目的

荧光分子断层成像(FMT)可以通过绘制完整生物体中荧光报告分子的生物分布来提供有价值的分子信息。在过去的十年中,已经引入了各种原型 FMT 系统。然而,它们都没有发展成为常规生物医学研究的标准工具。本文的目的是开发一个软件包,可以自动完成完整的 FMT 重建过程。

方法

我们提出了荧光断层成像的智能工具包(STIFT),这是一个包含三个主要协议的综合平台:1)虚拟 FMT,即模拟数据的正向建模和重建;2)实际 FMT 数据采集的控制;3)实验 FMT 数据的重建。

结果

模拟和体模实验都显示出了含有荧光团的均匀和异质组织模拟体模的稳健重建结果。

结论

STIFT 可用于优化 FMT 实验,特别是优化照明模式。

意义

本文通过在模拟、实际实验和数据重建之间架起桥梁,促进了 FMT 实验的发展。

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