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

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 t...

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Bibliographic Details
Published in:IEEE transactions on radiation and plasma medical sciences 2019-03, Vol.3 (2), p.109-119
Main Authors: Lee, Hoyeon, Lee, Jongha, Kim, Hyeongseok, Cho, Byungchul, Cho, Seungryong
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Commercialization
Computed tomography
Convolution
Data sampling
Deep learning
Image processing
Image reconstruction
Interpolation
Iterative algorithms
Iterative methods
low-dose computed tomography (CT)
Machine learning
Mathematical analysis
Medical imaging
Missing data
Neural networks
Scanners
sparse-view CT
Synthesis
Training
view interpolation
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Summary: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.
ISSN:2469-7311
2469-7303
DOI:10.1109/TRPMS.2018.2867611