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Undersampled MR Image Reconstruction with Data-Driven Tight Frame.

Liu J, Wang S, Peng X, Liang D - Comput Math Methods Med (2015)

Bottom Line: By taking advantage of the efficiency and effectiveness of data-driven tight frame, DDTF-MRI trains an adaptive tight frame to sparsify the to-be-reconstructed MR image.Furthermore, a two-level Bregman iteration algorithm has been developed to solve the proposed model.The proposed method has been compared to two state-of-the-art methods on four datasets and encouraging performances have been achieved by DDTF-MRI.

View Article: PubMed Central - PubMed

Affiliation: Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

ABSTRACT
Undersampled magnetic resonance image reconstruction employing sparsity regularization has fascinated many researchers in recent years under the support of compressed sensing theory. Nevertheless, most existing sparsity-regularized reconstruction methods either lack adaptability to capture the structure information or suffer from high computational load. With the aim of further improving image reconstruction accuracy without introducing too much computation, this paper proposes a data-driven tight frame magnetic image reconstruction (DDTF-MRI) method. By taking advantage of the efficiency and effectiveness of data-driven tight frame, DDTF-MRI trains an adaptive tight frame to sparsify the to-be-reconstructed MR image. Furthermore, a two-level Bregman iteration algorithm has been developed to solve the proposed model. The proposed method has been compared to two state-of-the-art methods on four datasets and encouraging performances have been achieved by DDTF-MRI.

No MeSH data available.


Related in: MedlinePlus

(a) Reference. Reconstructed images from the radial sampling mask with accelerating factor R = 4 by (b) CSMRI-TV, (c) DLMRI, and (d) our proposed DDTF-MRI, respectively.
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fig6: (a) Reference. Reconstructed images from the radial sampling mask with accelerating factor R = 4 by (b) CSMRI-TV, (c) DLMRI, and (d) our proposed DDTF-MRI, respectively.

Mentions: In Figure 6, we evaluated the proposed method with another in vivo brain data, which contains more fine-detailed structures, using the radial sampling trajectory. The reconstructed results using CSMRI-TV, DLMRI, and DDTF-MRI with a higher acceleration factor R = 4 are displayed in Figures 6(b), 6(c), and 6(d), respectively. The zoom-in results are also provided in Figure 6. Compared to the reference image shown in Figure 6(a), it can be observed that the result obtained by CSMRI-TV suffers from blocky artifacts. Meanwhile, the reconstructed image obtained by DDTF-MRI is clearer and sharper than those reconstructed by CSMRI-TV and DLMRI. This reveals that our proposed method can provide a more accurate recovered image.


Undersampled MR Image Reconstruction with Data-Driven Tight Frame.

Liu J, Wang S, Peng X, Liang D - Comput Math Methods Med (2015)

(a) Reference. Reconstructed images from the radial sampling mask with accelerating factor R = 4 by (b) CSMRI-TV, (c) DLMRI, and (d) our proposed DDTF-MRI, respectively.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4495234&req=5

fig6: (a) Reference. Reconstructed images from the radial sampling mask with accelerating factor R = 4 by (b) CSMRI-TV, (c) DLMRI, and (d) our proposed DDTF-MRI, respectively.
Mentions: In Figure 6, we evaluated the proposed method with another in vivo brain data, which contains more fine-detailed structures, using the radial sampling trajectory. The reconstructed results using CSMRI-TV, DLMRI, and DDTF-MRI with a higher acceleration factor R = 4 are displayed in Figures 6(b), 6(c), and 6(d), respectively. The zoom-in results are also provided in Figure 6. Compared to the reference image shown in Figure 6(a), it can be observed that the result obtained by CSMRI-TV suffers from blocky artifacts. Meanwhile, the reconstructed image obtained by DDTF-MRI is clearer and sharper than those reconstructed by CSMRI-TV and DLMRI. This reveals that our proposed method can provide a more accurate recovered image.

Bottom Line: By taking advantage of the efficiency and effectiveness of data-driven tight frame, DDTF-MRI trains an adaptive tight frame to sparsify the to-be-reconstructed MR image.Furthermore, a two-level Bregman iteration algorithm has been developed to solve the proposed model.The proposed method has been compared to two state-of-the-art methods on four datasets and encouraging performances have been achieved by DDTF-MRI.

View Article: PubMed Central - PubMed

Affiliation: Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

ABSTRACT
Undersampled magnetic resonance image reconstruction employing sparsity regularization has fascinated many researchers in recent years under the support of compressed sensing theory. Nevertheless, most existing sparsity-regularized reconstruction methods either lack adaptability to capture the structure information or suffer from high computational load. With the aim of further improving image reconstruction accuracy without introducing too much computation, this paper proposes a data-driven tight frame magnetic image reconstruction (DDTF-MRI) method. By taking advantage of the efficiency and effectiveness of data-driven tight frame, DDTF-MRI trains an adaptive tight frame to sparsify the to-be-reconstructed MR image. Furthermore, a two-level Bregman iteration algorithm has been developed to solve the proposed model. The proposed method has been compared to two state-of-the-art methods on four datasets and encouraging performances have been achieved by DDTF-MRI.

No MeSH data available.


Related in: MedlinePlus