Stayman J Webster, Zbijewski Wojciech, Tilley Steven, Siewerdsen Jeffrey
Dept. of Biomedical Eng., Johns Hopkins University, Baltimore, MD USA 21205.
Proc SPIE Int Soc Opt Eng. 2014 Mar 19;9033:903335. doi: 10.1117/12.2043067.
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.
统计重建方法的成功及剂量利用率的提高，部分源于它们能够纳入测量过程和噪声物理的复杂模型。尽管统计方法前景广阔，但典型的测量模型忽略了模糊效应，而且几乎所有当前方法都假定测量是独立的——忽视了噪声相关性以及提高图像质量的一个潜在途径。在一些成像系统中，如基于平板的锥束CT，这种相关性和模糊可能是限制最大可实现空间分辨率和噪声性能的主要因素。在这项工作中，我们提出了一种新颖的正则化广义最小二乘重建方法，该方法在投影数据中纳入了系统模糊和相关噪声的模型。我们在模拟研究中证明，这种方法可以突破那些未对这些物理效应建模的方法的传统空间分辨率限制。此外，与其他尝试在没有相关模型的情况下进行去模糊的方法相比，所提出的方法能够实现更优的噪声分辨率权衡。
