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Proceedings Paper

Dual-energy CT reconstruction using deep mutual-domain knowledge for basis decomposition and denoising
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Paper Abstract

As a quantitative CT imaging technique, the dual-energy CT (DECT) imaging method attracts a lot of research interests. However, material decomposition from high energy (HE) and low energy (LE) data may suffer from magnified noise, resulting in severe degradation of image quality and decomposition accuracy. To overcome these challenges, this study presents a novel DECT material decomposition method based on deep neural network (DNN). In particular, this new DNN integrates the CT image reconstruction task and the nonlinear material decomposition procedures into one single network. This end-to-end network consists of three compartments: the sinogram domain decomposition compartment, the user-defined analytical domain transformation operation (OP) compartment, and the image domain decomposition compartment. By design, both the first and third compartments are responsible for complicated nonlinear material decomposition, while denoising the DECT images. Natural images are used to synthesized the dual-energy data with assumed certain volume fractions and density distributions. By doing so, the burden of collecting clinical DECT data can be significantly reduced, therefore the new DECT reconstruction framework becomes more easy to be implemented. Both numerical and experimental validation results demonstrate that the proposed DNN based DECT reconstruction algorithm can generate high quality basis images with improved accuracy.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 113124C (16 March 2020); doi: 10.1117/12.2547736
Show Author Affiliations
Yikun Zhang, Shenzhen Institutes of Advanced Technology (China)
Ting Su, Shenzhen Institutes of Advanced Technology (China)
Jiongtao Zhu, Shenzhen Institutes of Advanced Technology (China)
Yang Chen, Southeast Univ. (China)
Hairong Zheng, Shenzhen Institutes of Advanced Technology (China)
Dong Liang, Shenzhen Institutes of Advanced Technology (China)
Yongshuai Ge, Shenzhen Institutes of Advanced Technology (China)

Published in SPIE Proceedings Vol. 11312:
Medical Imaging 2020: Physics of Medical Imaging
Guang-Hong Chen; Hilde Bosmans, Editor(s)

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