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

Lu Y, Kowarschik M, Huang X, Xia Y, Choi JH, Chen S, Hu S, Ren Q, Fahrig R, Hornegger J, Maier A (2019)

Publication Type: Journal article

Publication year: 2019

Journal

Book Volume: 46

Pages Range: 689-703

Journal Issue: 2

DOI: 10.1002/mp.13317

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.

Authors with CRIS profile

Involved external institutions

Siemens Healthineers Germany (DE) Shanghai Jiao Tong University / 上海交通大学 China (CN) Ewha Womans University / 이화여자대학교 Korea, Republic of (KR) Peking University (PKU) / 北京大学 China (CN) Stanford University United States (USA) (US)

How to cite

APA:

Lu, Y., Kowarschik, M., Huang, X., Xia, Y., Choi, J.-H., Chen, S.,... Maier, A. (2019). A learning-based material decomposition pipeline for multi-energy x-ray imaging. Medical Physics, 46(2), 689-703. https://doi.org/10.1002/mp.13317

MLA:

Lu, Yanye, et al. "A learning-based material decomposition pipeline for multi-energy x-ray imaging." Medical Physics 46.2 (2019): 689-703.

BibTeX: Download