View-interpolation of sparsely sampled sinogram using convolutional neural network

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Spare-view sampling and its associated iterative image reconstruction in computed tomography have actively investigated. Sparse-view CT technique is a viable option to low-dose CT, particularly in cone-beam CT (CBCT) applications, with advanced iterative image reconstructions with varying degrees of image artifacts. One of the artifacts that may occur in sparse-view CT is the streak artifact in the reconstructed images. Another approach has been investigated for sparse-view CT imaging by use of the interpolation methods to fill in the missing view data and that reconstructs the image by an analytic reconstruction algorithm. In this study, we developed an interpolation method using convolutional neural network (CNN), which is one of the widely used deep-learning methods, to find missing projection data and compared its performances with the other interpolation techniques.
Publisher
SPIE
Issue Date
2017-02-13
Language
English
Citation

Medical Imaging 2017: Image Processing

DOI
10.1117/12.2254244
URI
http://hdl.handle.net/10203/273017
Appears in Collection
NE-Conference Papers(학술회의논문)
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