US Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging and Applied Mathematics, 10903 New Hampshire Ave., Silver Spring, MD 20993, USA.
Phys Med Biol. 2011 Oct 7;56(19):6223-42. doi: 10.1088/0031-9155/56/19/005. Epub 2011 Sep 6.
Multi-slice computed tomography (MSCT) scanners have become popular volumetric imaging tools. Deterministic and random properties of the resulting CT scans have been studied in the literature. Due to the large number of voxels in the three-dimensional (3D) volumetric dataset, full characterization of the noise covariance in MSCT scans is difficult to tackle. However, as usage of such datasets for quantitative disease diagnosis grows, so does the importance of understanding the noise properties because of their effect on the accuracy of the clinical outcome. The goal of this work is to study noise covariance in the helical MSCT volumetric dataset. We explore possible approximations to the noise covariance matrix with reduced degrees of freedom, including voxel-based variance, one-dimensional (1D) correlation, two-dimensional (2D) in-plane correlation and the noise power spectrum (NPS). We further examine the effect of various noise covariance models on the accuracy of a prewhitening matched filter nodule size estimation strategy. Our simulation results suggest that the 1D longitudinal, 2D in-plane and NPS prewhitening approaches can improve the performance of nodule size estimation algorithms. When taking into account computational costs in determining noise characterizations, the NPS model may be the most efficient approximation to the MSCT noise covariance matrix.
多层螺旋计算机断层扫描（MSCT）扫描仪已成为流行的容积成像工具。文献中已经研究了由此产生的 CT 扫描的确定性和随机性特性。由于三维（3D）容积数据集的体素数量众多，因此很难全面描述 MSCT 扫描中的噪声协方差。然而，随着此类数据集在定量疾病诊断中的使用越来越多，了解噪声特性的重要性也随之增加，因为它们会影响临床结果的准确性。这项工作的目的是研究螺旋 MSCT 容积数据集中的噪声协方差。我们探索了具有较少自由度的噪声协方差矩阵的可能近似，包括体素方差、一维（1D）相关、二维（2D）平面内相关和噪声功率谱（NPS）。我们进一步研究了各种噪声协方差模型对预白化匹配滤波器结节大小估计策略准确性的影响。我们的模拟结果表明，1D 纵向、2D 平面内和 NPS 预白化方法可以提高结节大小估计算法的性能。在考虑确定噪声特征的计算成本时，NPS 模型可能是 MSCT 噪声协方差矩阵的最有效近似。
