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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) Original image. (b) Random sampling mask. (c) PSNR values versus accelerating factors.
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fig3: (a) Original image. (b) Random sampling mask. (c) PSNR values versus accelerating factors.

Mentions: We then employed these three methods to reconstruct the sagittal brain and test their sensitivity to the acceleration factors. Figure 3(c) plots the PSNR values versus different accelerating factors under the random sampling trajectory. It shows that DDTF-MRI performs better than the other two methods at all the acceleration factors. For a visual comparison, Figure 4 provides the reconstructed results at accelerating factor 3. For a close-up comparison, we have enlarged two edge parts, from which we can see DDTF-MRI provides clearer details.


Undersampled MR Image Reconstruction with Data-Driven Tight Frame.

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

(a) Original image. (b) Random sampling mask. (c) PSNR values versus accelerating factors.
© Copyright Policy
Related In: Results  -  Collection

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

fig3: (a) Original image. (b) Random sampling mask. (c) PSNR values versus accelerating factors.
Mentions: We then employed these three methods to reconstruct the sagittal brain and test their sensitivity to the acceleration factors. Figure 3(c) plots the PSNR values versus different accelerating factors under the random sampling trajectory. It shows that DDTF-MRI performs better than the other two methods at all the acceleration factors. For a visual comparison, Figure 4 provides the reconstructed results at accelerating factor 3. For a close-up comparison, we have enlarged two edge parts, from which we can see DDTF-MRI provides clearer details.

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