Graph Based Sinogram Denoising for Tomographic Reconstructions
This work addresses noise reduction in tomographic reconstructions for medical imaging, but it appears incremental as it applies an existing graph-based signal processing approach to a specific domain.
The authors tackled the problem of noisy and incomplete data in low-dose CT reconstructions by proposing a graph-based sinogram denoising algorithm, which improved the performance of analytical and iterative reconstruction methods by minimizing error measures and enhancing accuracy.
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.