Ring artifact removal method with generative adversarial network for the refraction-contrast computed tomography

Abstract

Refraction-contrast X-ray computed tomography (RCT), which provides a high-contrast image of biological soft tissues, has recently been used in pathology and micro anatomy research. One of the characteristics of RCT is that the projection obtained by RCT system represents the first differentiation of the object's radon transformation. Therefore, the reconstruction of RCT involves an integration process of the projection before conventional back-projection. However, this process causes noise generated by the detector or refraction angle estimation errors to propagate throughout the projection image, which leads to increase ring artifacts (RA) on the reconstructed image. Although various methods for RA removal have been developed in the absorption-based CT (ACT) field, it is difficult to eliminate surface widespread RA on RCT image. In this research, we propose a RA removal method based on conditional generative adversarial network (cGAN) for RCT. This method incorporates a sparse property of Laplacian RCT image which is easily obtained by reconstructing the differential projection. To demonstrate the effectiveness of this method, we applied the proposed and conventional ACT-based methods to simulation data of numerical phantoms and compared them by the root mean square error and the structural similarity. In the result, we demonstrated the proposed method can remove RA better than conventional methods.