The Resolution Matrix in Tomographic Multiplexing: Optimization of Inter-Parameter Cross-Talk, Relative Quantitation, and Localization.
The Resolution Matrix in Tomographic Multiplexing: Optimization of Inter-Parameter Cross-Talk, Relative Quantitation, and Localization.
复制标题
层析成像多重分析中的分辨率矩阵：参数间串扰、相对定量和定位的优化。
DOI：
10.1109/tbme.2018.2889043
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Kumar,AnandTN
中科院分区：
文献类型：
--
作者：
Hou,StevenS;Bacskai,BrianJ;Kumar,AnandTN
我们使用一个分辨率矩阵为基础的贝叶斯框架比较反演方法层析荧光寿命复用在一个扩散介质，如生物tissue.MethodsWe考虑三个反演方法;基于时域数据的多指数分析的渐近时域（ATD）方法，作为最小误差解的直接时域（DTD）方法，以及串扰约束时域（CCTD）反演，这是最大限度地减少误差和串扰的优化问题的解决方案。我们比较这些方法使用Monte Carlo模拟和时域荧光测量与组织模仿phanadherence.ResultsThe ATD方法提供了高精度的相对定量和空间定位的两个荧光团嵌入在一个18毫米厚的混浊介质，浓度比高达1：4.25。DTD导致相对定量和定位的显著误差。与DTD相比，CCTD提供了更高的定量准确度，与ATD相比，空间分辨率更好。我们提出了一个严格的理论基础，这些结果，并提供了一个完整的推导的CCTD估计。贝叶斯分析还导致了一个公式的快速计算的DTD逆算子为大规模的断层扫描measurement.ConclusionThe ATD和CCTD反演方法提供了显着的优势，超过DTD准确估计多个重叠的fluorophores.SignificanceTime域荧光断层扫描，使用零串扰估计，可以作为一个强大的工具，用于量化多个荧光标记的生物过程。贝叶斯框架可以应用于一般的多参数反问题的多个重叠参数的定量估计。
ObjectiveWe use a resolution matrix-based Bayesian framework to compare inversion methods for tomographic fluorescence lifetime multiplexing in a diffuse medium, such as biological tissue.MethodsWe consider three inversion methods; an asymptotic time domain (ATD) approach, based on a multiexponential analysis of time domain data, a direct time domain (DTD) approach, which is a minimum error solution, and a cross-talk constrained time domain (CCTD) inversion, which is a solution to an optimization problem that minimizes both error and cross-talk. We compare these methods using Monte Carlo simulations and time domain fluorescence measurements with tissue-mimicking phantoms.ResultsThe ATD approach provides high accuracy of relative quantitation and spatial localization of two fluorophores embedded in a 18-mm thick turbid medium, with concentration ratios of up to 1:4.25. DTD leads to significant errors in relative quantitation and localization. CCTD provides improved quantitation accuracy over DTD, and better spatial resolution compared to ATD. We present a rigorous theoretical basis for these results and provide a complete derivation of the CCTD estimator. The Bayesian analysis also leads to a formula for rapid computation of the DTD inverse operator for large-scale tomography measurements.ConclusionThe ATD and CCTD inversion methods provide significant advantages over DTD for accurately estimating multiple overlapping fluorophores.SignificanceTime domain fluorescence tomography, using zero cross-talk estimators, can serve as a powerful tool for quantifying multiple fluorescently labeled biological processes. The Bayesian framework presented here can be applied to general multiparameter inverse problems for the quantitative estimation of multiple overlapping parameters.