Generalized Least-Squares CT Reconstruction with Detector Blur and Correlated Noise Models.
Generalized Least-Squares CT Reconstruction with Detector Blur and Correlated Noise Models.
复制标题
具有检测器模糊和相关噪声模型的广义最小二乘CT重建。
DOI：
10.1117/12.2043067
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发表时间：
2014-03-19
期刊：
影响因子：
--
通讯作者：
Siewerdsen J
中科院分区：
文献类型：
--
作者：
Stayman JW;Zbijewski W;Tilley S 2nd;Siewerdsen J
统计重建方法的成功和改进的剂量利用部分源于它们能够结合测量过程和噪声的物理的复杂模型。尽管统计方法前景看好，但典型的测量模型忽略了模糊效应，并且几乎所有当前的方法都假设独立的测量-忽略噪声相关性和改善图像质量的潜在途径。在一些成像系统中，例如基于平板的锥束CT，这种相关性和模糊可能是限制可实现的最大空间分辨率和噪声性能的主要因素。在这项工作中，我们提出了一种新的正则化广义最小二乘重建方法，该方法同时考虑了投影数据中的系统模糊和相关噪声模型。我们在仿真研究中证明，这种方法可以突破传统方法的空间分辨率限制，这些方法不能对这些物理效应进行建模。此外，与尝试在没有相关模型的情况下去模糊的其他方法相比，所提出的方法可以在噪声分辨率方面找到更好的折衷。
The success and improved dose utilization of statistical reconstruction methods arises, in part, from their ability to incorporate sophisticated models of the physics of the measurement process and noise. Despite the great promise of statistical methods, typical measurement models ignore blurring effects, and nearly all current approaches make the presumption of independent measurements – disregarding noise correlations and a potential avenue for improved image quality. In some imaging systems, such as flat-panel-based cone-beam CT, such correlations and blurs can be a dominant factor in limiting the maximum achievable spatial resolution and noise performance. In this work, we propose a novel regularized generalized least-squares reconstruction method that includes models for both system blur and correlated noise in the projection data. We demonstrate, in simulation studies, that this approach can break through the traditional spatial resolution limits of methods that do not model these physical effects. Moreover, in comparison to other approaches that attempt deblurring without a correlation model, superior noise-resolution trade-offs can be found with the proposed approach.
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