Though effective and computationally efficient algorithms have been developed, the commonly utilized filtered backprojection (FBP) approach to computed tomography (CT) reconstruction suffers from artifact production in sparse-view applications. Within the past few years, convolutional neural networks (CNNs) have been applied to enhance sparse-view reconstruction in CT imaging. Using a network trained on sparse-view FBP reconstructions, the artifacts introduced by undersampling the imaging space can be removed. In this paper, we investigate specific choices in the implementation of the CNN, including the network architecture, training parameters, and data preprocessing, to determine effects on the images produced by the network. Our proposed algorithm and implementation strategies improve upon the use of FBP algorithms alone by removing artifacts produced during sparse-view CT reconstruction.

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Cain Gantt, Yuanwei Jin, and Enyue Lu, "Deep neural networks for sparse-view filtered backprojection imaging," Proc. SPIE 10990, Computational Imaging IV, 109900W (Presented at SPIE Defense + Commercial Sensing: April 15, 2019; Published: 13 May 2019); https://doi.org/10.1117/12.2513681.