A learning-based material decomposition pipeline for multi-energy x-ray imaging
A learning-based material decomposition pipeline for multi-energy x-ray imaging
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DOI：
10.1002/mp.13317
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发表时间：
2019-02-01
期刊：
影响因子：
3.8
通讯作者：
Maier, Andreas
中科院分区：
文献类型：
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作者：
Lu, Yanye;Kowarschik, Markus;Maier, Andreas
利用多能X射线成像技术，材料分解技术可以在X射线成像中对不同材料进行表征。然而，材料分解的性能受到分解模型的准确性的限制。由于X射线成像系统中存在非理想效应，很难明确地建立材料分解的成像系统模型。作为一种替代方案，本文探讨了使用机器学习方法的可行性材料分解tasks.MethodsIn这项工作中，我们提出了一个基于学习的管道来执行材料分解。在该流水线中，特征提取的步骤被实现为集成更多信息特征，例如邻近信息，以促进材料分解任务，并且采用连续交错采样的保持验证的步骤来执行模型评估和选择。我们证明了我们提出的管道材料分解能力与有前途的机器学习算法在模拟和实验中，其中的算法是人工神经网络（ANN），随机树，REPTree和随机森林。使用模拟XCAT体模和拟人躯干体模对性能进行定量评价。为了评估所提出的方法，两种基于测量的材料分解方法被用作比较研究的参考方法。此外，还研究了基于深度学习的解决方案，以完成这项工作作为一个全面的比较机器学习解决方案的材料decomposition.ResultsIn模拟研究和实验研究，引入的机器学习算法能够训练模型的材料分解任务。通过引入邻域信息，大大提高了各种机器学习算法的性能。与最先进的方法相比，ANN在仿真研究中的性能在无噪声情况下提高了24%以上，在有噪声情况下提高了169%以上，而随机森林的性能分别提高了40%和165%以上。同样，在实验研究中，ANN的性能在去噪场景中提高了42%以上，在原始场景中提高了45%以上，而随机森林的性能分别提高了33%和40%以上，ConclusionsThe proposed pipeline is able to build general material decomposition models for different scenarios，并通过仿真和实验进行了定量评价。与参考方法相比，适当的特征和机器学习算法可以显著提高材料分解性能。结果表明，使用机器学习方法进行材料分解是可行的和有前途的，我们的研究将有助于未来的临床应用。
PurposeBenefiting from multi-energy x-ray imaging technology, material decomposition facilitates the characterization of different materials in x-ray imaging. However, the performance of material decomposition is limited by the accuracy of the decomposition model. Due to the presence of nonideal effects in x-ray imaging systems, it is difficult to explicitly build the imaging system models for material decomposition. As an alternative, this paper explores the feasibility of using machine learning approaches for material decomposition tasks.MethodsIn this work, we propose a learning-based pipeline to perform material decomposition. In this pipeline, the step of feature extraction is implemented to integrate more informative features, such as neighboring information, to facilitate material decomposition tasks, and the step of hold-out validation with continuous interleaved sampling is employed to perform model evaluation and selection. We demonstrate the material decomposition capability of our proposed pipeline with promising machine learning algorithms in both simulation and experimentation, the algorithms of which are artificial neural network (ANN), Random Tree, REPTree and Random Forest. The performance was quantitatively evaluated using a simulated XCAT phantom and an anthropomorphic torso phantom. In order to evaluate the proposed method, two measurement-based material decomposition methods were used as the reference methods for comparison studies. In addition, deep learning-based solutions were also investigated to complete this work as a comprehensive comparison of machine learning solution for material decomposition.ResultsIn both the simulation study and the experimental study, the introduced machine learning algorithms are able to train models for the material decomposition tasks. With the application of neighboring information, the performance of each machine learning algorithm is strongly improved. Compared to the state-of-the-art method, the performance of ANN in the simulation study is an improvement of over 24% in the noiseless scenarios and over 169% in the noisy scenario, while the performance of the Random Forest is an improvement of over 40% and 165%, respectively. Similarly, the performance of ANN in the experimental study is an improvement of over 42% in the denoised scenario and over 45% in the original scenario, while the performance of Random Forest is an improvement by over 33% and 40%, respectively.ConclusionsThe proposed pipeline is able to build generic material decomposition models for different scenarios, and it was validated by quantitative evaluation in both simulation and experimentation. Compared to the reference methods, appropriate features and machine learning algorithms can significantly improve material decomposition performance. The results indicate that it is feasible and promising to perform material decomposition using machine learning methods, and our study will facilitate future efforts toward clinical applications.