Data correlation based noise level estimation for cone beam projection data.
Data correlation based noise level estimation for cone beam projection data.
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DOI：
10.3233/xst-17266
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发表时间：
2017
影响因子：
3
通讯作者：
Mou X
中科院分区：
文献类型：
--
作者：
Bai T;Yan H;Ouyang L;Staub D;Wang J;Jia X;Jiang SB;Mou X
在正则化迭代重建算法中，正则化参数的选择依赖于锥束投影数据的噪声水平。我们的目的是提出一种算法来估计锥束投影数据的噪声水平。首先在傅里叶域导出了锥束投影数据的数据相关性，在此基础上实现了信号与噪声的解耦。然后提取噪声并对其进行平均以进行估计。提出了一种基于估计噪声水平的自适应正则化参数选择策略。为了进行性能验证，进行了仿真和真实数据研究。在锥束投影数据的三维傅立叶域中，存在一个近似为零能量的双楔形区。对于噪声水平估计结果，在解析/MC/聚光灯模式仿真实验中，该算法的平均相对误差分别为0.8%、0.14%和0.24%，优于基于均匀区域的算法和基于变换的算法。实际研究表明，估计的噪声水平与暴露水平成反比，即对于短扫描和半扇模式，对数对数图中的斜率为−1.0197和−1.049。引入的正则化参数选择策略可以提供良好的重建图像质量。该算法基于锥束投影数据在傅立叶域内的数据相关性，能够准确、稳健地估计锥束投影数据的噪声水平。估计的噪声水平可用于自适应地选择正则化参数。
In regularized iterative reconstruction algorithms, the selection of regularization parameter depends on the noise level of cone beam projection data. Our aim is to propose an algorithm to estimate the noise level of cone beam projection data. We first derived the data correlation of cone beam projection data in the Fourier domain, based on which, the signal and the noise were decoupled. Then the noise was extracted and averaged for estimation. An adaptive regularization parameter selection strategy was introduced based on the estimated noise level. Simulation and real data studies were conducted for performance validation. There exists an approximately zero-energy double-wedge area in the 3D Fourier domain of cone beam projection data. As for the noise level estimation results, the averaged relative errors of the proposed algorithm in the analytical/MC/spotlight-mode simulation experiments were 0.8%, 0.14% and 0.24%, respectively, and outperformed the homogeneous area based as well as the transformation based algorithms. Real studies indicated that the estimated noise levels were inversely proportional to the exposure levels, i.e., the slopes in the log-log plot were −1.0197 and −1.049 with respect to the short-scan and half-fan modes. The introduced regularization parameter selection strategy could deliver promising reconstructed image qualities. Based on the data correlation of cone beam projection data in Fourier domain, the proposed algorithm could estimate the noise level of cone beam projection data accurately and robustly. The estimated noise level could be used to adaptively select the regularization parameter.