Graph Based Sinogram Denoising for Tomographic Reconstructions

TL;DR

This paper introduces a graph-based sinogram denoising algorithm that enhances the quality of tomographic reconstructions by exploiting the piecewise smooth structure of sinograms, improving accuracy across various methods.

Contribution

The paper presents a novel graph signal processing approach for sinogram denoising, outperforming standard filters in improving reconstruction accuracy in low-dose CT.

Findings

Graph denoising minimizes error measures.

Improves accuracy of FBP, ART, and SIRT reconstructions.

Enhances reconstruction quality in noisy, limited data scenarios.

Abstract

Limited data and low dose constraints are common problems in a variety of tomographic reconstruction paradigms which lead to noisy and incomplete data. Over the past few years sinogram denoising has become an essential pre-processing step for low dose Computed Tomographic (CT) reconstructions. We propose a novel sinogram denoising algorithm inspired by the modern field of signal processing on graphs. Graph based methods often perform better than standard filtering operations since they can exploit the signal structure. This makes the sinogram an ideal candidate for graph based denoising since it generally has a piecewise smooth structure. We test our method with a variety of phantoms and different reconstruction methods. Our numerical study shows that the proposed algorithm improves the performance of analytical filtered back-projection (FBP) and iterative methods ART (Kaczmarz) and SIRT (Cimmino).We observed that graph denoised sinogram always minimizes the error measure and improves the accuracy of the solution as compared to regular reconstructions.