Share Email Print
cover

Proceedings Paper

Fully convolutional networks based sinogram correction for metal artifact reduction
Author(s): Linlin Zhu; Yu Han; Ziheng Li; Xiaoqi Xi; Lei Li; Bin Yan; Mingwan Zhu
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Computed tomography (CT) has been extensively used in nondestructive testing, medical diagnosis, etc. In the field of modern medicine, metal implants are widely used in people's daily life, and the serious artifacts in CT reconstruction images caused by metal implants cannot be ignored. Sinogram contains the most realistic projection information of patients. Processing in the sinogram domain directly can make the effective information maximum extent preserved. In this paper, we propose a novel method based on full convolutional network (FCN) for metal artifact reduction in the sinogram domain. The networks we introduced use the complete sinogram data to learn a mapping function to correct the metal-corrupted sinogram data. The network takes the metal-corrupted sinogram as the input and takes the artifact-free sinogram as the target. Compared with the existing deep learning-based CT artifact reduction methods, our work just uses the sinogram information to correct the metal artifacts. The proposed network can process images of different sizes. Our initial results on a simulated dataset to demonstrate the potential effectiveness of this new approach to suppressing artifacts.

Paper Details

Date Published: 18 December 2019
PDF: 5 pages
Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 1134208 (18 December 2019); doi: 10.1117/12.2542909
Show Author Affiliations
Linlin Zhu, PLA Strategic Support Force Information Engineering Univ. (China)
Yu Han, PLA Strategic Support Force Information Engineering Univ. (China)
Ziheng Li, PLA Strategic Support Force Information Engineering Univ. (China)
Xiaoqi Xi, PLA Strategic Support Force Information Engineering Univ. (China)
Lei Li, PLA Strategic Support Force Information Engineering Univ. (China)
Bin Yan, PLA Strategic Support Force Information Engineering Univ. (China)
Mingwan Zhu, PLA Strategic Support Force Information Engineering Univ. (China)


Published in SPIE Proceedings Vol. 11342:
AOPC 2019: AI in Optics and Photonics
John Greivenkamp; Jun Tanida; Yadong Jiang; HaiMei Gong; Jin Lu; Dong Liu, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray