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

A Sinogram Inpainting Method based on Generative Adversarial Network for Limited-angle Computed Tomography