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Sparse CT reconstruction based on multi-direction anisotropic total variation (MDATV).

Li H, Chen X, Wang Y, Zhou Z, Zhu Q, Yu D - Biomed Eng Online (2014)

Bottom Line: The numerical and real data experiments demonstrate that MDATV based iterative reconstruction improved the quality of restored image.MDATV regularization can sufficiently use the prior information of image sparsity and edge information simultaneously.By incorporating more prior information, MDATV based approach could reconstruct the image more exactly.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China. xdchen@tju.edu.cn.

ABSTRACT

Background: The sparse CT (Computed Tomography), inspired by compressed sensing, means to introduce a prior information of image sparsity into CT reconstruction to reduce the input projections so as to reduce the potential threat of incremental X-ray dose to patients' health. Recently, many remarkable works were concentrated on the sparse CT reconstruction from sparse (limited-angle or few-view style) projections. In this paper we would like to incorporate more prior information into the sparse CT reconstruction for improvement of performance. It is known decades ago that the given projection directions can provide information about the directions of edges in the restored CT image. ATV (Anisotropic Total Variation), a TV (Total Variation) norm based regularization, could use the prior information of image sparsity and edge direction simultaneously. But ATV can only represent the edge information in few directions and lose much prior information of image edges in other directions.

Methods: To sufficiently use the prior information of edge directions, a novel MDATV (Multi-Direction Anisotropic Total Variation) is proposed. In this paper we introduce the 2D-IGS (Two Dimensional Image Gradient Space), and combined the coordinate rotation transform with 2D-IGS to represent edge information in multiple directions. Then by incorporating this multi-direction representation into ATV norm we get the MDATV regularization. To solve the optimization problem based on the MDATV regularization, a novel ART (algebraic reconstruction technique) + MDATV scheme is outlined. And NESTA (NESTerov's Algorithm) is proposed to replace GD (Gradient Descent) for minimizing the TV-based regularization.

Results: The numerical and real data experiments demonstrate that MDATV based iterative reconstruction improved the quality of restored image. NESTA is more suitable than GD for minimization of TV-based regularization.

Conclusions: MDATV regularization can sufficiently use the prior information of image sparsity and edge information simultaneously. By incorporating more prior information, MDATV based approach could reconstruct the image more exactly.

Show MeSH
The Shepp-Logan phantom and edges. (a) The original Shepp-Logan phantom with display gray scale window of [0,1], (b) its horizontal edges Eh, (c) rotated horizontal edges Ehr, (d) gradient image with display gray scale window of [0,0.01], and (e) vertical edges Ev, (f) rotated vertical edges Evr. The edges are rotated counterclockwise by 45°.
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Figure 3: The Shepp-Logan phantom and edges. (a) The original Shepp-Logan phantom with display gray scale window of [0,1], (b) its horizontal edges Eh, (c) rotated horizontal edges Ehr, (d) gradient image with display gray scale window of [0,0.01], and (e) vertical edges Ev, (f) rotated vertical edges Evr. The edges are rotated counterclockwise by 45°.

Mentions: where Dv and Dh are the same as Di and Dj in (3) representing the vertical and the horizontal finite differential operators respectively. The Eh and Ev of Shepp-Logan phantom are shown in Figure 3(b) and (e). The original image is generated by the function ‘phantom(128)’ in MATLAB®, as shown in Figure 3(a).


Sparse CT reconstruction based on multi-direction anisotropic total variation (MDATV).

Li H, Chen X, Wang Y, Zhou Z, Zhu Q, Yu D - Biomed Eng Online (2014)

The Shepp-Logan phantom and edges. (a) The original Shepp-Logan phantom with display gray scale window of [0,1], (b) its horizontal edges Eh, (c) rotated horizontal edges Ehr, (d) gradient image with display gray scale window of [0,0.01], and (e) vertical edges Ev, (f) rotated vertical edges Evr. The edges are rotated counterclockwise by 45°.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4127084&req=5

Figure 3: The Shepp-Logan phantom and edges. (a) The original Shepp-Logan phantom with display gray scale window of [0,1], (b) its horizontal edges Eh, (c) rotated horizontal edges Ehr, (d) gradient image with display gray scale window of [0,0.01], and (e) vertical edges Ev, (f) rotated vertical edges Evr. The edges are rotated counterclockwise by 45°.
Mentions: where Dv and Dh are the same as Di and Dj in (3) representing the vertical and the horizontal finite differential operators respectively. The Eh and Ev of Shepp-Logan phantom are shown in Figure 3(b) and (e). The original image is generated by the function ‘phantom(128)’ in MATLAB®, as shown in Figure 3(a).

Bottom Line: The numerical and real data experiments demonstrate that MDATV based iterative reconstruction improved the quality of restored image.MDATV regularization can sufficiently use the prior information of image sparsity and edge information simultaneously.By incorporating more prior information, MDATV based approach could reconstruct the image more exactly.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China. xdchen@tju.edu.cn.

ABSTRACT

Background: The sparse CT (Computed Tomography), inspired by compressed sensing, means to introduce a prior information of image sparsity into CT reconstruction to reduce the input projections so as to reduce the potential threat of incremental X-ray dose to patients' health. Recently, many remarkable works were concentrated on the sparse CT reconstruction from sparse (limited-angle or few-view style) projections. In this paper we would like to incorporate more prior information into the sparse CT reconstruction for improvement of performance. It is known decades ago that the given projection directions can provide information about the directions of edges in the restored CT image. ATV (Anisotropic Total Variation), a TV (Total Variation) norm based regularization, could use the prior information of image sparsity and edge direction simultaneously. But ATV can only represent the edge information in few directions and lose much prior information of image edges in other directions.

Methods: To sufficiently use the prior information of edge directions, a novel MDATV (Multi-Direction Anisotropic Total Variation) is proposed. In this paper we introduce the 2D-IGS (Two Dimensional Image Gradient Space), and combined the coordinate rotation transform with 2D-IGS to represent edge information in multiple directions. Then by incorporating this multi-direction representation into ATV norm we get the MDATV regularization. To solve the optimization problem based on the MDATV regularization, a novel ART (algebraic reconstruction technique) + MDATV scheme is outlined. And NESTA (NESTerov's Algorithm) is proposed to replace GD (Gradient Descent) for minimizing the TV-based regularization.

Results: The numerical and real data experiments demonstrate that MDATV based iterative reconstruction improved the quality of restored image. NESTA is more suitable than GD for minimization of TV-based regularization.

Conclusions: MDATV regularization can sufficiently use the prior information of image sparsity and edge information simultaneously. By incorporating more prior information, MDATV based approach could reconstruct the image more exactly.

Show MeSH