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24 February 2017View-interpolation of sparsely sampled sinogram using convolutional neural network
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.