%0 Journal Article
%T Fluorescence Lifetime Imaging Based on Neural Network Algorithm<br>基于神经网络原理的荧光寿命成像研究
%A SUI Cheng-hua
%A ZHOU Ming-hua
%A <br>隋成华
%A 周明华
%J 生物物理学报
%D 2005
%I 
%X Fluorescence lifetime imaging is a rather effective and powerful method that can be used to analyze complex biological tissues and molecules. However, the traditional approaches of data analysis did not provide a good fit data of auto fluorescence decay data due to the lack of consideration of a continuous distribution of fluorescence lifetime generated from interactions either among fluorophores or between the fluorophores and their environments. In the paper, it was put forward that a neural network algorithm was likely to provide a truer representation of the underlying fluorescence dynamics. The nonlinear model of fluorescence dynamics can be established effectively by using this method. It has advantages of robustness (the initial value of fit is free), stronger ability to treat nonlinear model, better fitting precision and much less processing time. As compared with those of single exponential and multi-exponential decay functions, the novel model can yield the better goodness of fit and more effective calculation using the data from multi-well plate assays of interesting fluorophores chemically and biologically. In the same time, the potential application of neural network algorithm to fluorescence lifetime imaging was discussed.
%K Biomedical photonics
%K Fluorescence lifetime imaging
%K Fluorescence decay profile
%K Neural network algorithm
%K Data fitting<br>生物医学光子学
%K 荧光寿命成像
%K 荧光衰减过程
%K 神经网络：数据拟合
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=90BA3D13E7F3BC869AC96FB3DA594E3FE34FBF7B8BC0E591&jid=E0C9D9BBED813D6674AC13E942EAC86D&aid=DA196E7FCCF452F9&yid=2DD7160C83D0ACED&vid=659D3B06EBF534A7&iid=38B194292C032A66&sid=527AEE9F3446633A&eid=1D67BE204FBF4800&journal_id=1000-6737&journal_name=生物物理学报&referenced_num=0&reference_num=18