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

TL;DR

This paper introduces a GAN-based inpainting method to restore missing sinogram data in limited-angle CT, significantly reducing artifacts and improving image reconstruction quality.

Contribution

The paper proposes a novel GAN-based approach for sinogram inpainting tailored for limited-angle CT, enhancing reconstruction accuracy over existing methods.

Findings

Reduces artifacts caused by missing projection data.

Effective for 60-degree limited scanning angles.

Improves reconstructed image quality using GAN inpainting.

Abstract

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.