Performance Enhancement of Pharmacokinetic Diffuse Fluorescence Tomography by Use of Adaptive Extended Kalman Filtering.
Performance Enhancement of Pharmacokinetic Diffuse Fluorescence Tomography by Use of Adaptive Extended Kalman Filtering.
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使用自适应扩展卡尔曼滤波增强药代动力学漫射荧光断层扫描的性能
DOI：
10.1155/2015/739459
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发表时间：
2015
影响因子：
--
通讯作者：
Gao F
中科院分区：
文献类型：
--
作者：
Wang X;Wu L;Yi X;Zhang Y;Zhang L;Zhao H;Gao F
由于健康和病变组织之间血管形成的生理和形态差异，药代动力学弥散荧光断层扫描（DFT）可以为肿瘤诊断和分期提供对比增强和全面的信息。在这种情况下，基于扩展卡尔曼滤波（EKF）的方法显示出许多优点，包括准确的建模，在线估计的多参数，和普遍适用于任何光学荧光团。然而，传统的EKF的性能高度依赖于精确的和不可访问的先验知识的初始值。针对上述问题，提出了一种基于二房室模型的自适应EKF方法，该方法利用可变遗忘因子补偿初始状态的不准确性，并强调当前数据的影响。仿真结果表明，与传统EKF和增强EKF相比，该算法在量化、噪声鲁棒性和初始化无关性方面具有更好的动态速率估计性能。进一步的三维数字小鼠模型的数值实验验证了该方法的有效性，适用于现实的生物系统。
Due to both the physiological and morphological differences in the vascularization between healthy and diseased tissues, pharmacokinetic diffuse fluorescence tomography (DFT) can provide contrast-enhanced and comprehensive information for tumor diagnosis and staging. In this regime, the extended Kalman filtering (EKF) based method shows numerous advantages including accurate modeling, online estimation of multiparameters, and universal applicability to any optical fluorophore. Nevertheless the performance of the conventional EKF highly hinges on the exact and inaccessible prior knowledge about the initial values. To address the above issues, an adaptive-EKF scheme is proposed based on a two-compartmental model for the enhancement, which utilizes a variable forgetting-factor to compensate the inaccuracy of the initial states and emphasize the effect of the current data. It is demonstrated using two-dimensional simulative investigations on a circular domain that the proposed adaptive-EKF can obtain preferable estimation of the pharmacokinetic-rates to the conventional-EKF and the enhanced-EKF in terms of quantitativeness, noise robustness, and initialization independence. Further three-dimensional numerical experiments on a digital mouse model validate the efficacy of the method as applied in realistic biological systems.
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影响因子：
10.6
作者：
Sourbron, Steven
通讯作者：
Sourbron, Steven
影响因子：
1.9
作者：
Milstein, AB;Oh, S;Millane, RP
通讯作者：
Millane, RP
影响因子：
82.9
作者：
Ntziachristos, Vasilis;Tung, Ching-Hsuan;Weissleder, Ralph
通讯作者：
Weissleder, Ralph
影响因子：
6.8
作者：
Boutayeb, M;Aubry, D
通讯作者：
Aubry, D
DOI：
10.1364/josaa.22.001357
发表时间：
2005-07-01
影响因子：
1.9
作者：
Milstein, AB;Webb, KJ;Bouman, CA
通讯作者：
Bouman, CA