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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 575-589 |
| Number of pages | 15 |
| Journal | Journal of Computational Mathematics |
| Volume | 34 |
| Issue number | 6 |
| DOIs | |
| State | Published - Nov 1 2016 |
| Externally published | Yes |
Fingerprint
Keywords
- Bregmanized operator splitting algorithm
- Data driven tight frame
- Limited tomography
- Sinogram extrapolation
- Wavelet frame
- X-ray computed tomography
ASJC Scopus subject areas
- Computational Mathematics
Cite this
Limited tomography reconstruction via tight frame and simultaneous sinogram extrapolation. / Choi, Jae Kyu; Dong, Bin; Zhang, Xiaoqun.
In: Journal of Computational Mathematics, Vol. 34, No. 6, 01.11.2016, p. 575-589.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Limited tomography reconstruction via tight frame and simultaneous sinogram extrapolation
AU - Choi, Jae Kyu
AU - Dong, Bin
AU - Zhang, Xiaoqun
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
UR - http://www.scopus.com/inward/record.url?scp=85009881956&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85009881956&partnerID=8YFLogxK
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 -