Sparsity Constrained Mixture Modeling for the Estimation of Kinetic Parameters in Dynamic PET.
Sparsity Constrained Mixture Modeling for the Estimation of Kinetic Parameters in Dynamic PET.
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DOI：
10.1109/tmi.2013.2283229
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发表时间：
2014-01
影响因子：
10.6
通讯作者：
Leahy RM
中科院分区：
文献类型：
--
作者：
Lin Y;Haldar JP;Li Q;Conti PS;Leahy RM
动态PET中动力学参数的估计和分析经常受到组织异质性和部分容积效应的干扰。我们提出了一个新的动态PET的约束模型，以解决这些限制。所提出的配方采用了显式的混合模型，其中每个图像体素表示为不同的纯组织类型与不同的时间动态的混合物。我们使用Cramér-Rao下界来证明先验信息的使用对于稳定该模型的参数估计是重要的。因此，我们提出了一个约束制定的估计问题，我们使用两阶段算法解决。在第一阶段中，稀疏信号处理方法被应用于从噪声PET时间序列中估计不同组织隔室的速率参数。在第二阶段中，使用空间规则性和分数混合约束的组合来估计不同时间活动曲线的组织分数和线性参数。一个块坐标下降算法相结合的流形搜索鲁棒地估计这些参数。模拟和实验动态PET数据的方法进行评估。
The estimation and analysis of kinetic parameters in dynamic PET is frequently confounded by tissue heterogeneity and partial volume effects. We propose a new constrained model of dynamic PET to address these limitations. The proposed formulation incorporates an explicit mixture model in which each image voxel is represented as a mixture of different pure tissue types with distinct temporal dynamics. We use Cramér-Rao lower bounds to demonstrate that the use of prior information is important to stabilize parameter estimation with this model. As a result, we propose a constrained formulation of the estimation problem that we solve using a two-stage algorithm. In the first stage, a sparse signal processing method is applied to estimate the rate parameters for the different tissue compartments from the noisy PET time series. In the second stage, tissue fractions and the linear parameters of different time activity curves are estimated using a combination of spatial-regularity and fractional mixture constraints. A block coordinate descent algorithm is combined with a manifold search to robustly estimate these parameters. The method is evaluated with both simulated and experimental dynamic PET data.