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Pseudo dual energy CT imaging using deep learning-based framework: basic material estimation
Author(s): Yuting Liao; Yongbo Wang; Sui Li; Ji He; Dong Zeng; Zhaoying Bian; Jianhua Ma
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Paper Abstract

Dual energy computed tomography (DECT) usually scans the object twice using different energy spectrum, and then DECT is able to get two unprecedented material decompositions by directly performing signal decomposition. In general, one is the water equivalent fraction and other is the bone equivalent fraction. It is noted that the material decomposition often depends on two or more different energy spectrum. In this study, we present a deep learning-based framework to obtain basic material images directly form single energy CT images via cascade deep convolutional neural networks (CD-ConvNet). We denote this imaging procedure as pseudo DECT imaging. The CD-ConvNet is designed to learn the non-linear mapping from the measured energy-specific CT images to the desired basic material decomposition images. Specifically, the output of the former convolutional neural networks (ConvNet) in the CD-ConvNet is used as part of inputs for the following ConvNet to produce high quality material decomposition images. Clinical patient data was used to validate and evaluate the performance of the presented CD-ConvNet. Experimental results demonstrate that the presented CD-ConvNet can yield qualitatively and quantitatively accurate results when compared against gold standard. We conclude that the presented CD-ConvNet can help to improve research utility of CT in quantitative imaging, especially in single energy CT.

Paper Details

Date Published: 9 March 2018
PDF: 5 pages
Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105734N (9 March 2018); doi: 10.1117/12.2293237
Show Author Affiliations
Yuting Liao, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Yongbo Wang, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Sui Li, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Ji He, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Dong Zeng, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Zhaoying Bian, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Jianhua Ma, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)


Published in SPIE Proceedings Vol. 10573:
Medical Imaging 2018: Physics of Medical Imaging
Joseph Y. Lo; Taly Gilat Schmidt; Guang-Hong Chen, Editor(s)

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