Yan Jianhua, Chu-Shern Jason Lim, Loi Hoi Yin, Khor Lih Kin, Sinha Arvind K, Quek Swee Tian, Tham Ivan W K, Townsend David
A*STAR-NUS, Clinical Imaging Research Center, Singapore
A*STAR-NUS, Clinical Imaging Research Center, Singapore.
J Nucl Med. 2015 Nov;56(11):1667-73. doi: 10.2967/jnumed.115.156927. Epub 2015 Jul 30.
Evaluation of tumor heterogeneity based on texture parameters has recently attracted much interest in the PET imaging community. However, the impact of reconstruction settings on texture parameters is unclear, especially relating to time-of-flight and point-spread function modeling. Their effects on 55 texture features (TFs) and 6 features based on first-order statistics (FOS) were investigated. Standardized uptake value (SUV) measures were also evaluated as peak SUV (SUVpeak), maximum SUV, and mean SUV (SUVmean).
This study retrospectively recruited 20 patients with lesions in the lung who underwent whole-body (18)F-FDG PET/CT. The coefficient of variation (COV) of each feature across different reconstructions was calculated.
SUVpeak, SUVmean, 18 TFs, and 1 FOS were the most robust (COV ≤ 5%) whereas skewness, cluster shade, and zone percentage were the least robust (COV > 20%) with respect to reconstruction algorithms using default settings. Heterogeneity parameters had different sensitivities to iteration number. Twenty-four parameters including SUVpeak and SUVmean exhibited variation with a COV less than 5%; 28 parameters, including maximum SUV, showed variation with a COV in the range of 5%-10%. In addition, skewness, cluster shade, and zone percentage were the most sensitive to iteration number. In terms of sensitivity to full width at half maximum (FWHM), 15 TFs and 1 FOS had the best performance with a COV less than 5%, whereas SUVpeak and SUVmean had a COV between 5% and 10%. Grid size had the largest impact on image features, which was demonstrated by only 11 features, including SUVpeak and SUVmean, having a COV less than 10%.
Different image features have different sensitivities to reconstruction settings. Iteration number and FWHM of the gaussian filter have a similar impact on the image features. Grid size has a larger impact on the features than iteration number and FWHM. The features that exhibited large variations such as skewness in FOS, cluster shade, and zone percentage should be used with caution. The entropy in FOS, difference entropy, inverse difference normalized, inverse difference moment normalized, low gray-level run emphasis, high gray-level run emphasis, and low gray-level zone emphasis are the most robust features.
基于纹理参数评估肿瘤异质性最近在PET成像领域引起了广泛关注。然而，重建设置对纹理参数的影响尚不清楚，尤其是与飞行时间和点扩散函数建模相关的影响。研究了它们对55个纹理特征（TFs）和6个基于一阶统计量（FOS）的特征的影响。还评估了标准化摄取值（SUV）测量值，如峰值SUV（SUVpeak）、最大SUV和平均SUV（SUVmean）。
本研究回顾性招募了20例肺部有病变且接受了全身（18）F-FDG PET/CT检查的患者。计算了不同重建情况下每个特征的变异系数（COV）。
对于使用默认设置的重建算法，SUVpeak、SUVmean、18个TFs和1个FOS最稳健（COV≤5%），而偏度、聚类阴影和区域百分比最不稳健（COV>20%）。异质性参数对迭代次数有不同的敏感性。包括SUVpeak和SUVmean在内的24个参数的变异系数小于5%；包括最大SUV在内的28个参数的变异系数在5%-10%范围内。此外，偏度、聚类阴影和区域百分比对迭代次数最敏感。就对半高宽（FWHM）的敏感性而言，15个TFs和1个FOS表现最佳，变异系数小于5%，而SUVpeak和SUVmean的变异系数在5%-10%之间。网格大小对图像特征的影响最大，只有包括SUVpeak和SUVmean在内的11个特征的变异系数小于10%证明了这一点。
不同的图像特征对重建设置有不同的敏感性。高斯滤波器的迭代次数和FWHM对图像特征有类似的影响。网格大小对特征的影响比对迭代次数和FWHM的影响更大。对于FOS中的偏度、聚类阴影和区域百分比等表现出较大变化的特征应谨慎使用。FOS中的熵、差分熵、归一化逆差分、归一化逆差分矩、低灰度游程强调、高灰度游程强调和低灰度区域强调是最稳健的特征。
