Hybrid Weighted l<sub>1</sub>-Total Variation Constrained Reconstruction for MR Image

ZHAO Di; DU Huiqian; MEI Wenbo

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Article Navigation > Chinese Journal of Electronics > 2014 > 23(4): 747-752

ZHAO Di, DU Huiqian, MEI Wenbo, “Hybrid Weighted l1-Total Variation Constrained Reconstruction for MR Image,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 747-752, 2014,

Citation:

ZHAO Di, DU Huiqian, MEI Wenbo, “Hybrid Weighted l₁-Total Variation Constrained Reconstruction for MR Image,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 747-752, 2014,

ZHAO Di, DU Huiqian, MEI Wenbo, “Hybrid Weighted l1-Total Variation Constrained Reconstruction for MR Image,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 747-752, 2014,

Citation:

ZHAO Di, DU Huiqian, MEI Wenbo, “Hybrid Weighted l₁-Total Variation Constrained Reconstruction for MR Image,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 747-752, 2014,

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Hybrid Weighted l₁-Total Variation Constrained Reconstruction for MR Image

School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

Funds: This work is supported by Natural Science Foundation of China (No.61077022).

Received Date: 2013-04-01
Rev Recd Date: 2014-02-01
Publish Date: 2014-10-05

Abstract

Abstract

Compressed sensing based Magnetic resonance (MR) image reconstruction can be done by minimizing the sum of least square data fitting item, the Total variation (TV) and l₁ norm regularizations. In this paper, inspired by the conventional constrained reconstruction model, we propose a hybrid weighted l₁-TV minimization method to reconstruct MR image. We introduce the iterative mechanism to update the weights for l₁ and TV norms adaptively. The weights vary at each element of the image matrix according to the presented weights selection strategy. Experiments on Shepp-Logan phantom and practical MR images demonstrate the proposed method can preserve image details and obtain improved reconstruction quality compared to the traditional CS-MRI method and other weighted methods.
- Compressed sensing (CS),
- Magnetic resonance imaging (MRI),
- Image reconstruction,
- Weighted l₁ norm,
- Weighted Total variation (TV)

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References(19)

References

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