The aim of this work is to investigate the used of Transformer architectures in radial image reconstruction. While most deep-learning image reconstruction methods are based on convolutional neural networks (CNNs), recent advances in computer vision suggest that Transformer architecture may provide a favorable alternative in many vision tasks. In this work, we demonstrate that Transformer architectures can be used for sinogram interpolation and yield results comparable to CNNs.
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