TY - GEN
T1 - A Dual-Energy Metal Artifact Redcution Method for DECT Image Reconstruction
AU - Lyu, Tianling
AU - Zhao, Wei
AU - Gao, Wei
AU - Zhu, Jian
AU - Xi, Yan
AU - Chen, Yang
AU - Zhu, Wentao
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Metal implants are one of the culprits for image quality degradation in CT imaging, introducing so-called metal artifacts. With the help of the virtual-monochromatic imaging technique, dual-energy CT has been proven to be effective in metal artifact reduction. However, the virtual monochromatic images with suppressed metal artifacts show reduced CNR compared to polychromatic images. To remove metal artifacts on polychromatic images, we propose a dual-energy NMAR (deNMAR) algorithm in this paper that adds material decomposition to the widely used NMAR framework. The dual energy sinograms are first decomposed into water and bone sinograms, and metal regions are replaced with water on the reconstructed material maps. Prior sinograms are constructed by polyenergetically forward projecting the material maps with corresponding spectra, and they are used to guide metal trace interpolation in the same way as in the NMAR algorithm. We performed experiments on authentic human body phantoms, and the results show that the proposed deNMAR algorithm achieves better performance in tissue restoration compared to other compelling methods. Tissue boundaries become clear around metal implants, and CNR rises to 2.58 from ∼1.70 on 80 kV images compared to other dual-energy-based algorithms.
AB - Metal implants are one of the culprits for image quality degradation in CT imaging, introducing so-called metal artifacts. With the help of the virtual-monochromatic imaging technique, dual-energy CT has been proven to be effective in metal artifact reduction. However, the virtual monochromatic images with suppressed metal artifacts show reduced CNR compared to polychromatic images. To remove metal artifacts on polychromatic images, we propose a dual-energy NMAR (deNMAR) algorithm in this paper that adds material decomposition to the widely used NMAR framework. The dual energy sinograms are first decomposed into water and bone sinograms, and metal regions are replaced with water on the reconstructed material maps. Prior sinograms are constructed by polyenergetically forward projecting the material maps with corresponding spectra, and they are used to guide metal trace interpolation in the same way as in the NMAR algorithm. We performed experiments on authentic human body phantoms, and the results show that the proposed deNMAR algorithm achieves better performance in tissue restoration compared to other compelling methods. Tissue boundaries become clear around metal implants, and CNR rises to 2.58 from ∼1.70 on 80 kV images compared to other dual-energy-based algorithms.
KW - NMAR
KW - dual-energy CT
KW - material decomposition
KW - metal artifact reduction
UR - https://www.scopus.com/pages/publications/85179639141
U2 - 10.1109/EMBC40787.2023.10340221
DO - 10.1109/EMBC40787.2023.10340221
M3 - 会议稿件
C2 - 38083063
AN - SCOPUS:85179639141
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
BT - 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023
Y2 - 24 July 2023 through 27 July 2023
ER -