Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, 259 Wen-Hua First Road, Kwei-Shan, Tao-Yuan, 333, Taiwan.
Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, Tao-Yuan, Taiwan.
Eur Radiol. 2021 Oct;31(10):7865-7875. doi: 10.1007/s00330-021-07943-5. Epub 2021 Apr 14.
Quantum noise is a random process in X-ray-based imaging systems. We addressed and measured the uncertainty of radiomics features against this quantum noise in computed tomography (CT) images.
A clinical multi-detector CT scanner, two homogeneous phantom sets, and four heterogeneous samples were used. A solid tumor tissue removed from a male BALB/c mouse was included. We the placed phantom sets on the CT scanning table and repeated 20 acquisitions with identical imaging settings. Regions of interest were delineated for feature extraction. Statistical quantities-average, standard deviation, and percentage uncertainty-were calculated from these 20 repeated scans. Percentage uncertainty was used to measure and quantify feature stability against quantum noise. Twelve radiomics features were measured. Random noise was added to study the robustness of machine learning classifiers against feature uncertainty.
We found the ranges of percentage uncertainties from homogeneous soft tissue phantoms, homogeneous bone phantoms, and solid tumor tissue to be 0.01-2138%, 0.02-15%, and 0.18-16%, respectively. Overall, it was found that the CT features ShortRunHighGrayLevelEmpha (SRHGE) (0.01-0.18%), ShortRunLowGrayLevelEmpha (SRLGE) (0.01-0.41%), LowGrayLevelRunEmpha (LGRE) (0.01-0.39%), and LongRunLowGrayLevelEmpha (LRLGE) (0.02-0.66%) were the most stable features against the inherent quantum noise. The most unstable features were cluster shade (1-2138%) and max probability (1-16%). The impact of random noise to the prediction accuracy by different machine learning classifiers was found to be between 0 and 12%.
Twelve features were used for uncertainty measurements. The upper and lower bounds of percentage uncertainties were determined. The quantum noise effect on machine learning classifiers is model dependent.
• Quantum noise is a random process and is intrinsic to X-ray-based imaging systems. This inherent quantum noise creates unpredictable fluctuations in the gray-level intensities of image pixels. Extra cautions and further validations are strongly recommended when unstable radiomics features are selected by a predictive model for disease classification or treatment outcome prognosis. • We addressed and used the statistical quantity of percentage uncertainty to measure the uncertainty of radiomics features against the inherent quantum noise in computed tomography (CT) images. • A clinical multi-detector CT scanner, two homogeneous phantom sets, and four heterogeneous samples were used in the stability measurement. A solid tumor tissue removed from a male BALB/c mouse was included in the heterogeneous sample.
量子噪声是基于 X 射线的成像系统中的一种随机过程。我们针对计算机断层扫描（CT）图像中的这种量子噪声，对放射组学特征的不确定性进行了研究和测量。
使用了临床多探测器 CT 扫描仪、两个均匀体模集和四个非均匀样本。包括从雄性 BALB/c 小鼠中切除的实体瘤组织。我们将体模集放在 CT 扫描台上，并使用相同的成像参数重复采集 20 次。为了提取特征，我们划定了感兴趣的区域。从这 20 次重复扫描中计算出了统计量——平均值、标准差和百分比不确定性。百分比不确定性用于测量和量化特征对量子噪声的稳定性。共测量了 12 个放射组学特征。添加随机噪声以研究机器学习分类器对特征不确定性的稳健性。
我们发现，均匀软组织体模、均匀骨体模和实体瘤组织的百分比不确定性范围分别为 0.01-2138%、0.02-15%和 0.18-16%。总体而言，我们发现 CT 特征 ShortRunHighGrayLevelEmpha（SRHGE）（0.01-0.18%）、ShortRunLowGrayLevelEmpha（SRLGE）（0.01-0.41%）、LowGrayLevelRunEmpha（LGRE）（0.01-0.39%）和 LongRunLowGrayLevelEmpha（LRLGE）（0.02-0.66%）是最稳定的特征。最不稳定的特征是聚类阴影（1-2138%）和最大概率（1-16%）。不同机器学习分类器的随机噪声对预测准确性的影响在 0 到 12%之间。
使用了 12 个特征进行不确定性测量。确定了百分比不确定性的上下限。量子噪声对机器学习分类器的影响取决于模型。
量子噪声是一种随机过程，是基于 X 射线的成像系统所固有的。这种固有量子噪声会在图像像素的灰度强度中产生不可预测的波动。当预测模型选择不稳定的放射组学特征用于疾病分类或治疗结果预测时，强烈建议谨慎并进一步验证。
我们针对计算机断层扫描（CT）图像中的固有量子噪声，使用百分比不确定性这一统计量来测量放射组学特征的不确定性。
在稳定性测量中，我们使用了临床多探测器 CT 扫描仪、两个均匀体模集和四个非均匀样本。包括从雄性 BALB/c 小鼠中切除的实体瘤组织。
