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Proceedings Paper • new

Synthesize monochromatic images in spectral CT by dual-domain deep learning
Author(s): Chuqing Feng; Zhiqiang Chen; Kejun Kang; Yuxiang Xing
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Paper Abstract

Spectral computed tomography (CT) with photon counting detectors (PCDs) can collect photons by setting different energy bins. It is well acknowledged that PCD-based spectral CT has great potential for lowering radiation dose and improve material discrimination. One critical processing in spectral CT is energy spectrum modelling or spectral information decomposition. In this work, we proposed a dual-domain deep learning (DDDL) method to calibrate a spectral CT system by a neural network. Without explicit energy spectrum and detector response model, we train a neural network to implicitly define the non-linear relationship in spectral CT. Virtual monochromatic attenuation maps are synthesized directly from polychromatic projections. Simulation and real experimental results verified the feasibilities and accuracies of the proposed method.

Paper Details

Date Published: 28 May 2019
PDF: 6 pages
Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 1107229 (28 May 2019); doi: 10.1117/12.2534921
Show Author Affiliations
Chuqing Feng, Tsinghua Univ. (China)
Zhiqiang Chen, Tsinghua Univ. (China)
Kejun Kang, Tsinghua Univ. (China)
Yuxiang Xing, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 11072:
15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
Samuel Matej; Scott D. Metzler, Editor(s)

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