View : 90 Download: 0

A learning-based material decomposition pipeline for multi-energy x-ray imaging

Title
A learning-based material decomposition pipeline for multi-energy x-ray imaging
Authors
Lu, YanyeKowarschik, MarkusHuang, XiaolinXia, YanChoi, Jang-HwanChen, ShuqingHu, ShiyangRen, QiushiFahrig, RebeccaHornegger, JoachimMaier, Andreas
Ewha Authors
최장환
SCOPUS Author ID
최장환scopus
Issue Date
2019
Journal Title
MEDICAL PHYSICS
ISSN
0094-2405JCR Link

2473-4209JCR Link
Citation
MEDICAL PHYSICS vol. 46, no. 2, pp. 689 - 703
Keywords
deep learningfeature extractionmachine learningmaterial decompositionmodel selectionmulti-energyspectral x-ray imaging
Publisher
WILEY
Indexed
SCI; SCIE; SCOPUS WOS scopus
Document Type
Article
Abstract
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.
DOI
10.1002/mp.13317
Appears in Collections:
엘텍공과대학 > 휴먼기계바이오공학부 > Journal papers
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE