Deep-Neural-Network-Based Sinogram Synthesis for Sparse-View CT Image Reconstruction

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Recently, a number of approaches to low-dose computed tomography (CT) have been developed and deployed in commercialized CT scanners. Tube current reduction is perhaps the most actively explored technology with advanced image reconstruction algorithms. Sparse data sampling is another viable option to the low-dose CT, and sparse-view CT has been particularly of interest among the researchers in CT community. Since analytic image reconstruction algorithms would lead to severe image artifacts, various iterative algorithms have been developed for reconstructing images from sparsely view-sampled projection data. However, iterative algorithms take much longer computation time than the analytic algorithms, and images are usually prone to different types of image artifacts that heavily depend on the reconstruction parameters. Interpolation methods have also been utilized to fill the missing data in the sinogram of sparse-view CT thus providing synthetically full data for analytic image reconstruction. In this paper, we introduce a deep-neural-network-enabled sinogram synthesis method for sparse-view CT, and show its outperformance to the existing interpolation methods and also to the iterative image reconstruction approach.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2019-03
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES, v.3, no.2, pp.109 - 119

ISSN
2469-7311
DOI
10.1109/TRPMS.2018.2867611
URI
http://hdl.handle.net/10203/269099
Appears in Collection
NE-Journal Papers(저널논문)
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