  The improvement of computed tomography (CT) image resolution is beneficial to
the subsequent medical diagnosis, but it is usually limited by the scanning
devices and great expense. Convolutional neural network (CNN)-based methods
have achieved promising ability in super-resolution. However, existing methods
mainly focus on the super-resolution of reconstructed image and do not fully
explored the approach of super-resolution from projection-domain. In this
paper, we studied the characteristic of projection and proposed a CNN-based
super-resolution method to establish the mapping relationship of low- and
high-resolution projection. The network label is high-resolution projection and
the input is its corresponding interpolation data after down sampling. FDK
algorithm is utilized for three-dimensional image reconstruction and one slice
of reconstruction image is taken as an example to evaluate the performance of
the proposed method. Qualitative and quantitative results show that the
proposed method is potential to improve the resolution of projection and
enables the reconstructed image with higher quality.
