Ring Artifacts Removal Based on Implicit Neural Representation of Sinogram Data

TL;DR

This paper introduces an unsupervised method using implicit neural representations to correct stripe artifacts in sinogram data, effectively reducing ring artifacts in CT images and improving image quality.

Contribution

It presents a novel INR-based approach that simultaneously models defective pixel responses and stripe features for artifact correction in sinogram data.

Findings

Significantly outperforms existing methods in ring artifact removal

Maintains high image clarity after correction

Operates effectively in an unsupervised iterative framework

Abstract

Inconsistent responses of X-ray detector elements lead to stripe artifacts in the sinogram data, which manifest as ring artifacts in the reconstructed CT images, severely degrading image quality. This paper proposes a method for correcting stripe artifacts in the sinogram data. The proposed method leverages implicit neural representation (INR) to correct defective pixel response values using implicit continuous functions and simultaneously learns stripe features in the angular direction of the sinogram data. These two components are combined within an optimization constraint framework, achieving unsupervised iterative correction of stripe artifacts in the projection domain. Experimental results demonstrate that the proposed method significantly outperforms current state-of-the-art techniques in removing ring artifacts while maintaining the clarity of CT images.