Variability in CT lung-nodule quantification: Effects of dose reduction and reconstruction methods on density and texture based features.
Variability in CT lung-nodule quantification: Effects of dose reduction and reconstruction methods on density and texture based features.
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DOI：
10.1118/1.4954845
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发表时间：
2016-08
期刊：
影响因子：
3.8
通讯作者：
McNitt-Gray MF
中科院分区：
文献类型：
--
作者：
Lo P;Young S;Kim HJ;Brown MS;McNitt-Gray MF
研究剂量水平和重建方法对 CT 肺结节计算的基于密度和纹理的特征的影响。这项研究有两个主要组成部分。在第一个组件中，以三个剂量水平扫描均匀的水体模，并使用四种传统的滤波反投影 (FBP) 和四种迭代重建 (IR) 方法重建图像，总共 24 种不同的采集和重建条件组合。在第二部分中，从作为临床实践一部分进行扫描的患者中获得了 33 个肺结节的原始投影（正弦图）数据，其中通过向在临床剂量水平（总共四个剂量水平）获取的正弦图中添加噪声来模拟低剂量采集，并使用一个 FBP 内核和两个 IR 内核针对总共 12 种情况进行重建。对于水体模，在参考条件下获得的一张参考图像上的水体模内的多个位置处创建球形感兴趣区域（ROI）。对于肺结节病例，根据在参考条件下获得的图像半自动（手动编辑）绘制每个结节的 ROI。所有 ROI 均应用于在不同条件下重建的相应图像。对于 17 个结节病例，进行了重复轮廓检查以评估重复性。针对所有 ROI 计算基于纹理特征（34 个特征）的直方图（8 个特征）和灰度共生矩阵 (GLCM)。对于肺结节病例，参考条件选择100%临床剂量，使用B45f内核进行FBP重建；将根据其他条件计算出的特征值与该参考条件进行比较。引入了一种度量（作者将其称为 Q）来评估不同条件下特征的稳定性，其定义为每个特征的再现性（跨条件）与重复性（跨重复轮廓）的比率。水模型结果表明，除直方图平均值外，在不同条件下计算的特征值之间存在很大差异。从肺结节计算的特征显示出类似的结果，直方图平均值是最稳健的特征（Q ≤ 1），平均值和标准差 Q 分别为 0.37 和 0.22。令人惊讶的是，直方图标准差和方差特征也非常稳健。一些 GLCM 特征在不同条件下也相当稳健，即 diff。方差、方差总和、平均值总和、方差和均值。除直方图平均值外，所有特征在至少一种 3% 剂量水平条件下的 Q 值均大于 1。正如预期的那样，直方图平均值是他们研究中最稳健的特征。采集和重建条件对 GLCM 特征的影响差异很大，但趋势是涉及强度和概率之间的乘积求和的特征更加稳健，除了少数例外。总体而言，如果在 CT 中使用多种剂量和重建条件对肺结节进行量化，则应注意密度和纹理特征的变化，否则量化结果的变化可能比结节本身更能反映采集和重建条件引起的变化。
To investigate the effects of dose level and reconstruction method on density and texture based features computed from CT lung nodules. This study had two major components. In the first component, a uniform water phantom was scanned at three dose levels and images were reconstructed using four conventional filtered backprojection (FBP) and four iterative reconstruction (IR) methods for a total of 24 different combinations of acquisition and reconstruction conditions. In the second component, raw projection (sinogram) data were obtained for 33 lung nodules from patients scanned as a part of their clinical practice, where low dose acquisitions were simulated by adding noise to sinograms acquired at clinical dose levels (a total of four dose levels) and reconstructed using one FBP kernel and two IR kernels for a total of 12 conditions. For the water phantom, spherical regions of interest (ROIs) were created at multiple locations within the water phantom on one reference image obtained at a reference condition. For the lung nodule cases, the ROI of each nodule was contoured semiautomatically (with manual editing) from images obtained at a reference condition. All ROIs were applied to their corresponding images reconstructed at different conditions. For 17 of the nodule cases, repeat contours were performed to assess repeatability. Histogram (eight features) and gray level co-occurrence matrix (GLCM) based texture features (34 features) were computed for all ROIs. For the lung nodule cases, the reference condition was selected to be 100% of clinical dose with FBP reconstruction using the B45f kernel; feature values calculated from other conditions were compared to this reference condition. A measure was introduced, which the authors refer to as Q, to assess the stability of features across different conditions, which is defined as the ratio of reproducibility (across conditions) to repeatability (across repeat contours) of each feature. The water phantom results demonstrated substantial variability among feature values calculated across conditions, with the exception of histogram mean. Features calculated from lung nodules demonstrated similar results with histogram mean as the most robust feature (Q ≤ 1), having a mean and standard deviation Q of 0.37 and 0.22, respectively. Surprisingly, histogram standard deviation and variance features were also quite robust. Some GLCM features were also quite robust across conditions, namely, diff. variance, sum variance, sum average, variance, and mean. Except for histogram mean, all features have a Q of larger than one in at least one of the 3% dose level conditions. As expected, the histogram mean is the most robust feature in their study. The effects of acquisition and reconstruction conditions on GLCM features vary widely, though trending toward features involving summation of product between intensities and probabilities being more robust, barring a few exceptions. Overall, care should be taken into account for variation in density and texture features if a variety of dose and reconstruction conditions are used for the quantification of lung nodules in CT, otherwise changes in quantification results may be more reflective of changes due to acquisition and reconstruction conditions than in the nodule itself.