Limited Tomography Reconstruction via Tight Frame and Sinogram Extrapolation

TL;DR

This paper introduces a novel CT reconstruction method from limited sinogram data using tight frame regularization and sinogram extrapolation, outperforming existing sparsity-based approaches in numerical simulations.

Contribution

The paper proposes a new approach combining tight frame regularization with sinogram extrapolation for full CT image reconstruction from limited data, which is more effective than previous sparsity-based methods.

Findings

Numerical simulations show improved image quality.

The method effectively reconstructs entire CT images from truncated sinograms.

Outperforms existing sparsity model-based approaches.

Abstract

X-ray computed tomography (CT) is one of widely used diagnostic tools for medical and dental tomographic imaging of the human body. However, the standard filtered backprojection reconstruction method requires the complete knowledge of the projection data. In the case of limited data, the inverse problem of CT becomes more ill-posed, which makes the reconstructed image deteriorated by the artifacts. In this paper, we consider two dimensional CT reconstruction using the horizontally truncated projections. Over the decades, the numerous results including the sparsity model based approach has enabled the reconstruction of the image inside the region of interest (ROI) from the limited knowledge of the data. However, unlike these existing methods, we try to reconstruct the entire CT image from the limited knowledge of the sinogram via the tight frame regularization and the simultaneous sinogram extrapolation. Our proposed model shows more promising numerical simulation results compared with the existing sparsity model based approach.