Sinogram synthesis using convolutional-neural-network for sparsely view-sampled CT

Abstract

Reducing the number of projections in computed tomography (CT) has been exploited as a low-dose option in conjunction with advanced iterative image reconstruction algorithms. While such iterative image reconstruction methods do provide useful images and valuable insights of the inverse imaging problems, it is an intriguing issue whether missing view projection data in the sinogram can be successfully recovered. There have been reported several approaches to interpolating the missing sinogram data. Deep-learning based super-resolution techniques in the field of natural image enhancement have been recently introduced and showed promising results. Inspired by the super-resolution techniques, we have earlier proposed a sinogram inpainting method that uses a convolutional-neural-network for sparsely viewsampled CT. Despite of the encouraging initial results, our previously proposed method had two drawbacks. The measured sinogram was contaminated in the process of filling the missing sinogram by the deep learning network. Additionally, the sum of the interpolated sinogram in the direction of detector row at each view angle was not preserved. In this study, we improve our previously developed deep-learning based sinogram synthesis method by adding new layers and modifying the size of receptive field in the deep learning network to overcome the above limitations. From the quantitative evaluations on the image accuracy and quality using real patients' CT images, we show that the new approach synthesizes more accurate sinogram and thus leads to higher quality of CT image than the previous one.