Case Center for Imaging Research, Case Western Reserve University, Cleveland, OH, United States of America. Department of Radiology, Case Western Reserve University, Cleveland, OH, United States of America.
Phys Med Biol. 2018 Jun 8;63(12):125001. doi: 10.1088/1361-6560/aac711.
The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Z), relative electron density (ρ ), mean excitation energy (I ), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 s. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency.
目的是开发和评估机器学习方法，从临床双能 CT 数据中生成有效原子序数 (Z)、相对电子密度 (ρ)、平均激发能 (I) 和相对阻止本领 (RSP) 的定量参数图。这些图谱可用于材料识别和辐射剂量计算。使用历史质心 (HC)、随机森林 (RF) 和人工神经网络 (ANN) 等机器学习方法，从已知的 13 种组织替代物的组成中计算出用于模拟体的理想输出参数图，来学习双能 CT 输入数据与理想输出参数图之间的关系。在训练和模型选择步骤之后，使用机器学习预测器从独立的模拟体和患者输入数据生成参数图。使用理想图谱评估精度和准确性。此过程重复进行了一系列不同的曝光剂量，并与作为参考的临床常用双能、基于物理的方法进行了性能比较。与参考方法相比，机器学习方法生成的参数图更准确和更精确。当使用最低曝光剂量的数据时，它们的性能优势尤为明显，仅为典型临床腹部 CT 采集的五分之一。RF 方法的准确性最高。相比之下，ANN 方法的准确性仅低 1%，但计算效率比 RF 高得多，能够在 15 秒内生成参数图。在生成参数图时，机器学习方法在准确性和抗噪能力方面优于参考方法，这鼓励进一步探索这些技术。在我们评估的方法中，由于准确性、出色的低噪声性能和计算效率的结合，ANN 是最适合临床使用的方法。
