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Limited Tomography Reconstruction via Tight Frame and Simultaneous Sinogram Extrapolation

Limited Tomography Reconstruction via Tight Frame and Simultaneous Sinogram Extrapolation
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Year:    2016

Author:    Jae Kyu Choi, Bin Dong, Xiaoqun Zhang

Journal of Computational Mathematics, Vol. 34 (2016), Iss. 6 : pp. 575–589

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 back-projection 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 projections truncated along the spatial direction in the Radon domain. 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.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/jcm.1605-m2016-0535

Journal of Computational Mathematics, Vol. 34 (2016), Iss. 6 : pp. 575–589

Published online:    2016-01

AMS Subject Headings:   

Copyright:    COPYRIGHT: © Global Science Press

Pages:    15

Keywords:    X-ray computed tomography Limited tomography Wavelet frame Data driven tight frame Bregmanized operator splitting algorithm Sinogram extrapolation.

Author Details

Jae Kyu Choi

Bin Dong

Xiaoqun Zhang

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