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

Journal article


Publication Details

Author(s): Lu Y, Kowarschik M, Huang X, Xia Y, Choi JH, Chen S, Hu S, Ren Q, Fahrig R, Hornegger J, Maier A
Journal: Medical Physics
Publication year: 2019
Volume: 46
Journal issue: 2
Pages range: 689-703
ISSN: 0094-2405
eISSN: 1522-8541


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.


FAU Authors / FAU Editors

Chen, Shuqing
Lehrstuhl für Informatik 5 (Mustererkennung)
Fahrig, Rebecca Prof. Dr.
Technische Fakultät
Hornegger, Joachim Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)
Hu, Shiyang
Lehrstuhl für Informatik 5 (Mustererkennung)
Lu, Yanye
Lehrstuhl für Informatik 5 (Mustererkennung)
Maier, Andreas Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)


External institutions with authors

Ewha Womans University / 이화여자대학교
Peking University (PKU) / 北京大学
Shanghai Jiao Tong University / 上海交通大学
Siemens Healthineers
Stanford University


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://dx.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: 

Last updated on 2019-16-07 at 09:03