TY - JOUR
T1 - Limited tomography reconstruction via tight frame and simultaneous sinogram extrapolation
AU - Choi, Jae Kyu
AU - Dong, Bin
AU - Zhang, Xiaoqun
N1 - Funding Information:
This work was partially supported by NSFC (No. 91330102) and Sino-German center grant (No. GZ1025) and 973 program (No. 2015CB856004).
PY - 2016/11/1
Y1 - 2016/11/1
N2 - 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.
AB - 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.
KW - Bregmanized operator splitting algorithm
KW - Data driven tight frame
KW - Limited tomography
KW - Sinogram extrapolation
KW - Wavelet frame
KW - X-ray computed tomography
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U2 - 10.4208/jcm.1605-m2016-0535
DO - 10.4208/jcm.1605-m2016-0535
M3 - Article
AN - SCOPUS:85009881956
VL - 34
SP - 575
EP - 589
JO - Journal of Computational Mathematics
JF - Journal of Computational Mathematics
SN - 0254-9409
IS - 6
ER -