Sharpness preserved sinogram synthesis using convolutional neural network for sparse-view CT imaging

Sparse view computed tomography (CT) is an effective way to lower the radiation exposure, but results in streaking artifacts in the constructed CT image due to insufficient projection views. Several approaches have been reported for full view sinogram synthesis by interpolating the missing data into the sparse-view sinogram. However, current interpolation methods tend to generate over-smoothed sinogram, which could not preserve the sharpness of the image. Such sharpness is often referred to the region boundaries or tissue texture and of high importance as clinical indicators. To address this issue, this paper aims to propose an efficient sharpness-preserve spare-view CT sinogram synthesis method based on convolutional neural network (CNN). The sharpness preserving is stressed by the zero-order and first-order difference based loss function in the model. This study takes advantage of the residual design to overcome the problem of degradation for our deep network (20 layers), which is capable of extracting high level information and dealing with large sample dimensions (672 x 672). The proposed model design and loss function achieved a better performance in both quantitative and qualitative evaluation comparing to current state-of-the-art works. This study also performs ablation test on the effect of different designs and researches on hyper-parameter settings in the loss function.