  Limited-angle computed tomography (CT) image reconstruction is a challenging
reconstruction problem in the fields of CT. With the development of deep
learning, the generative adversarial network (GAN) perform well in image
restoration by approximating the distribution of training sample data. In this
paper, we proposed an effective GAN-based inpainting method to restore the
missing sinogram data for limited-angle scanning. To estimate the missing data,
we design the generator and discriminator of the patch-GAN and train the
network to learn the data distribution of the sinogram. We obtain the
reconstructed image from the restored sinogram by filtered back projection and
simultaneous algebraic reconstruction technique with total variation.
Experimental results show that serious artifacts caused by missing projection
data can be reduced by the proposed method, and it is hopeful to solve the
reconstruction problem of 60 degrees limited scanning angle.
