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Blind Decomposition of Multi-spectral Fluorescence Lifetime Imaging Microscopy Data: Further Validation

机译:多光谱荧光寿命成像显微镜数据的盲分解:进一步验证

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Characterization of living tissue without the need for biopsies is the goal of several probe technologies such as Multi-spectral Fluorescence Lifetime Imaging Microscopy. This technique measures the mixed response from the endogenous fluorophores within an organic sample. This response is decomposed into the individual response from every constituent using a fully constrained linear unmixing algorithm: Blind End-member and Abundance Extraction (BEAE). Further validation of the method is needed specially when dealing with real laboratory samples. Moreover, the BEAE method incorporates a regularization parameter during the quadratic optimization procedure which has to be tuned to improve the estimation accuracy. Different values for the regularization parameter are tested using synthetic data at a signal-to-noise ratio of 10 dB and 15 dB. The relative error against the ideal end-members for each component is measured. Results show that the estimation accuracy in each end-member increases when the regularization parameter is around 0.75. Blind decomposition of m-FLIM data from coronary samples is also performed for validation purposes. The extracted fluorescence decays are identified as collagen, elastin and LDL responses. Histopatology slides are used as reference to validate the results. Synthetic simulation shows that the BEAE algorithm performs a more accurate estimation of the end-members profiles due to the regularization term. Furthermore, analysis performed on ex-vivo samples match the qualitative description provided by histopatology slides.
机译:在不需要活检的情况下,活组织的表征是几种探针技术的目的,例如多光谱荧光寿命显微镜显微镜。该技术测量来自有机样品内的内源荧光团的混合响应。使用完全约束的线性解混算法:盲端构件和丰度提取(Beae),将该响应分解成各个组成部分的单个响应。在处理真实实验室样本时,需要特别验证该方法。此外,BEAE方法包括在必须调整的二次优化过程中的正则化参数,以提高估计精度。使用合成数据以10dB和15dB的信噪比测试正则化参数的不同值。测量针对每个组件的理想结束构件的相对误差。结果表明,当正则化参数约为0.75时,每个端部构件中的估计精度都会增加。还针对验证目的进行了来自冠状动脉样本的M-Flim数据的盲分解。提取的荧光衰减被鉴定为胶原,弹性蛋白和LDL反应。组织图幻灯片用作参考以验证结果。合成仿真表明,由于正则化术语,BEAE算法对最终成员配置文件进行更准确估计。此外,对ex-Vivo样品进行的分析匹配组织图载玻片提供的定性描述。

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