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

Beitrag in einer Fachzeitschrift


Details zur Publikation

Autor(en): Lu Y, Kowarschik M, Huang X, Xia Y, Choi JH, Chen S, Hu S, Ren Q, Fahrig R, Hornegger J, Maier A
Zeitschrift: Medical Physics
Jahr der Veröffentlichung: 2019
Band: 46
Heftnummer: 2
Seitenbereich: 689-703
ISSN: 0094-2405


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-Autoren / FAU-Herausgeber

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)


Autor(en) der externen Einrichtung(en)
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


Zitierweisen

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: 

Zuletzt aktualisiert 2019-20-03 um 15:38