Direct reconstruction of kinetic parameter images from dynamic PET data
Direct reconstruction of kinetic parameter images from dynamic PET data
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DOI：
10.1109/tmi.2005.845317
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发表时间：
2005-05-01
影响因子：
10.6
通讯作者：
Sauer, K
中科院分区：
文献类型：
--
作者：
Kamasak, ME;Bouman, CA;Sauer, K
本文的目标是估计与高密度三维正电子发射断层扫描(PET)图像相对应的每个体素的动力学模型参数。通常，首先从每个测量时间的PET正弦图帧重建活动图像，然后通过将模型与重建的每个体素的时间活动响应进行拟合来估计动力学参数。然而，这种间接的动力学参数估计方法往往会降低信噪比(SNR)，因为需要将正弦图数据划分为单独的时间范围。1985年，Carson和Lange提出了一种基于期望最大化(EM)算法的直接参数重建方法，但没有实现。这种方法是“直接的”，因为它直接从正弦图数据估计最优的动力学参数，而不需要中间的重建步骤。然而，直接体素参数重建仍然是一个挑战，因为尚未解决的复杂的反演和空间正则化，在该文中，我们展示和评估了一种新的和有效的方法，直接体素重建运动参数图像的所有帧的PET数据。直接参数图像重建是在贝叶斯框架下提出的，并使用参数迭代坐标下降(PICD)算法来求解由此产生的优化问题[2]。PICD算法的计算效率很高，并且在生理相关参数的域中使用空间正则化来实现。我们在工作的小动物扫描仪上对大鼠头成像的实验模拟表明，与间接方法相比，直接参数重建可以在不显著增加计算量的情况下显著降低动力学参数估计的均方根误差(RMSE)。
Our goal in this paper is the estimation of kinetic model parameters for each voxel corresponding to a dense three-dimensional (3-D) positron emission tomography (PET) image. Typically, the activity images are first reconstructed from PET sinogram frames at each measurement time, and then the kinetic parameters are estimated by fitting a model to the reconstructed time-activity response of each voxel. However, this "indirect" approach to kinetic parameter estimation tends to reduce signal-to-noise ratio (SNR) because of the requirement that the sinogram data be divided into individual time frames.In 1985, Carson and Lange proposed [1], but did not implement, a method based on the expectation-maximization (EM) algorithm for direct parametric reconstruction. The approach is "direct" because it estimates the optimal kinetic parameters directly from the sinogram data, without an intermediate reconstruction step. However, direct voxel-wise parametric reconstruction remained a challenge due to the unsolved complexities of inversion and spatial regularization.In this paper, we demonstrate and evaluate a new and efficient method for direct voxel-wise reconstruction of kinetic parameter images using all frames of the PET data. The direct parametric image reconstruction is formulated in a Bayesian framework, and uses the parametric iterative coordinate descent (PICD) algorithm to solve the resulting optimization problem [2]. The PICD algorithm is computationally efficient and is implemented with spatial regularization in the domain of the physiologically relevant parameters. Our experimental simulations of a rat head imaged in a working small animal scanner indicate that direct parametric reconstruction can substantially reduce root-mean-squared error (RMSE) in the estimation of kinetic parameters, as compared to indirect methods, without appreciably increasing computation.