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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.

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Restored images from 11 views of noise-free (top row) and noisy projections (bottom row) with methods of ART-TV-GD, ART-TV-NESTA, ART-ATV-GD, ART-ATV-NESTA, ART-MDATV-GD, and ART-MDATV-NESTA (from left to right) after 100 iterations. The display gray scale window is [0,1].
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Figure 6: Restored images from 11 views of noise-free (top row) and noisy projections (bottom row) with methods of ART-TV-GD, ART-TV-NESTA, ART-ATV-GD, ART-ATV-NESTA, ART-MDATV-GD, and ART-MDATV-NESTA (from left to right) after 100 iterations. The display gray scale window is [0,1].

Mentions: The restored images from 11 views of noise-free and noisy projections are shown in Figure 6, and the restored images from 15 views of noise-free and noisy projections are shown in Figure 7. Both the observations of Figures 6 and 7 indicate that the TV and MDATV regularizations are obviously better than ATV regularization. There are some horizontal artifacts in the restored image of ATV methods, which is caused by the unbalanced regularization, while the projection data are uniformly distributed. It can also be observed that increased projection data prominently improved the image quality.


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)

Restored images from 11 views of noise-free (top row) and noisy projections (bottom row) with methods of ART-TV-GD, ART-TV-NESTA, ART-ATV-GD, ART-ATV-NESTA, ART-MDATV-GD, and ART-MDATV-NESTA (from left to right) after 100 iterations. The display gray scale window is [0,1].
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Restored images from 11 views of noise-free (top row) and noisy projections (bottom row) with methods of ART-TV-GD, ART-TV-NESTA, ART-ATV-GD, ART-ATV-NESTA, ART-MDATV-GD, and ART-MDATV-NESTA (from left to right) after 100 iterations. The display gray scale window is [0,1].
Mentions: The restored images from 11 views of noise-free and noisy projections are shown in Figure 6, and the restored images from 15 views of noise-free and noisy projections are shown in Figure 7. Both the observations of Figures 6 and 7 indicate that the TV and MDATV regularizations are obviously better than ATV regularization. There are some horizontal artifacts in the restored image of ATV methods, which is caused by the unbalanced regularization, while the projection data are uniformly distributed. It can also be observed that increased projection data prominently improved the image quality.

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