A learning‐based material decomposition pipeline for multi‐energy x‐ray imaging. Issue 2 (24th December 2018)

• Record Type: Journal Article
• Authors: Lu, Yanye; Kowarschik, Markus; Huang, Xiaolin; Xia, Yan; Choi, Jang‐Hwan; Chen, Shuqing; Hu, Shiyang; Ren, Qiushi; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas
• Is Part Of: Medical physics. Volume 46:Issue 2(2019)
• Page Start: 689
• Page End: 703
• Publication Date: 2018-12-24
• Subjects: deep learning -- feature extraction -- machine learning -- material decomposition -- model selection -- multi‐energy -- spectral x‐ray imaging; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.13317
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Benefiting 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. Methods: In 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. Results: In 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. Conclusions: The 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.