Neural Network-based Sinogram Upsampling in Real-measured CT Reconstruction

Computed tomography (CT) is one of the most popular non-invasive medical imaging modalities. A major downside of medical CT is the exposure of the patient to high-energy X-rays during image acquisition. One way to reduce the amount of ionising radiation is to record fewer projective views and then upsample the resulting subsampled sinogram. Post acquisition, this can be achieved through conventional sinogram interpolation algorithms or using neural networks. This paper compares the results of two upsampling network architectures with the results of conventional sinogram interpolation. We found that for subsampling factors two and four, the neural networks did not substantially improve the model predictions in terms of structured similarity and peak signal-to-noise ratio compared to conventional sinogram interpolation. This suggests that, for these subsampling factors and the given dataset, interpolation approximates the problem well enough.