Dynamic MRI reconstruction exploiting partial separability and t-SVD

Shuli Ma; Huiqian Du; Qiongzhi Wu; Wenbo Mei

doi:10.1109/ICBCB.2019.8854641

Dynamic MRI reconstruction exploiting partial separability and t-SVD

Shuli Ma, Huiqian Du^*, Qiongzhi Wu, Wenbo Mei

^*Corresponding author for this work

School of Integrated Circuits and Electronics

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Citations (Scopus)

Abstract

In this paper, we proposed a new method to reconstruct dynamic magnetic imaging (dMRI) data from highly undersampled k-t space measurements. First, we use the partial separability (PS) model to capture the spatiotemporal correlations of dMRI data. Then, we introduce a new tensor decomposition method named as tensor singular value decomposition (t-SVD) to the reconstruction problem. PS and low tensor multi-rank constrains are jointly enforced to reconstruct dynamic MRI data. We develop an efficient algorithm based on the alternating direction method of multipliers (ADMM) to solve the proposed optimization problem. The experimental results demonstrate the superior performance of the proposed method.

Original language	English
Title of host publication	Proceedings of 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology, ICBCB 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	179-184
Number of pages	6
ISBN (Electronic)	9781728106410
DOIs	https://doi.org/10.1109/ICBCB.2019.8854641
Publication status	Published - Mar 2019
Event	7th IEEE International Conference on Bioinformatics and Computational Biology, ICBCB 2019 - Hangzhou, China Duration: 21 Mar 2019 → 23 Mar 2019

Publication series

Name	Proceedings of 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology, ICBCB 2019

Conference

Conference	7th IEEE International Conference on Bioinformatics and Computational Biology, ICBCB 2019
Country/Territory	China
City	Hangzhou
Period	21/03/19 → 23/03/19

Keywords

Dynamic magnetic imaging
Low rank tensor
Partial separability
Tensor singular value decompositoin

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10.1109/ICBCB.2019.8854641

Cite this

Ma, S., Du, H., Wu, Q., & Mei, W. (2019). Dynamic MRI reconstruction exploiting partial separability and t-SVD. In Proceedings of 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology, ICBCB 2019 (pp. 179-184). Article 8854641 (Proceedings of 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology, ICBCB 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICBCB.2019.8854641

Ma, Shuli ; Du, Huiqian ; Wu, Qiongzhi et al. / Dynamic MRI reconstruction exploiting partial separability and t-SVD. Proceedings of 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology, ICBCB 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 179-184 (Proceedings of 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology, ICBCB 2019).

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title = "Dynamic MRI reconstruction exploiting partial separability and t-SVD",

abstract = "In this paper, we proposed a new method to reconstruct dynamic magnetic imaging (dMRI) data from highly undersampled k-t space measurements. First, we use the partial separability (PS) model to capture the spatiotemporal correlations of dMRI data. Then, we introduce a new tensor decomposition method named as tensor singular value decomposition (t-SVD) to the reconstruction problem. PS and low tensor multi-rank constrains are jointly enforced to reconstruct dynamic MRI data. We develop an efficient algorithm based on the alternating direction method of multipliers (ADMM) to solve the proposed optimization problem. The experimental results demonstrate the superior performance of the proposed method.",

keywords = "Dynamic magnetic imaging, Low rank tensor, Partial separability, Tensor singular value decompositoin",

author = "Shuli Ma and Huiqian Du and Qiongzhi Wu and Wenbo Mei",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 7th IEEE International Conference on Bioinformatics and Computational Biology, ICBCB 2019 ; Conference date: 21-03-2019 Through 23-03-2019",

year = "2019",

month = mar,

doi = "10.1109/ICBCB.2019.8854641",

language = "English",

series = "Proceedings of 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology, ICBCB 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "Proceedings of 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology, ICBCB 2019",

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Ma, S, Du, H , Wu, Q & Mei, W 2019, Dynamic MRI reconstruction exploiting partial separability and t-SVD. in Proceedings of 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology, ICBCB 2019., 8854641, Proceedings of 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology, ICBCB 2019, Institute of Electrical and Electronics Engineers Inc., pp. 179-184, 7th IEEE International Conference on Bioinformatics and Computational Biology, ICBCB 2019, Hangzhou, China, 21/03/19. https://doi.org/10.1109/ICBCB.2019.8854641

Dynamic MRI reconstruction exploiting partial separability and t-SVD. / Ma, Shuli; Du, Huiqian ; Wu, Qiongzhi et al.
Proceedings of 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology, ICBCB 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 179-184 8854641 (Proceedings of 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology, ICBCB 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Dynamic MRI reconstruction exploiting partial separability and t-SVD

AU - Ma, Shuli

AU - Du, Huiqian

AU - Wu, Qiongzhi

AU - Mei, Wenbo

PY - 2019/3

Y1 - 2019/3

N2 - In this paper, we proposed a new method to reconstruct dynamic magnetic imaging (dMRI) data from highly undersampled k-t space measurements. First, we use the partial separability (PS) model to capture the spatiotemporal correlations of dMRI data. Then, we introduce a new tensor decomposition method named as tensor singular value decomposition (t-SVD) to the reconstruction problem. PS and low tensor multi-rank constrains are jointly enforced to reconstruct dynamic MRI data. We develop an efficient algorithm based on the alternating direction method of multipliers (ADMM) to solve the proposed optimization problem. The experimental results demonstrate the superior performance of the proposed method.

AB - In this paper, we proposed a new method to reconstruct dynamic magnetic imaging (dMRI) data from highly undersampled k-t space measurements. First, we use the partial separability (PS) model to capture the spatiotemporal correlations of dMRI data. Then, we introduce a new tensor decomposition method named as tensor singular value decomposition (t-SVD) to the reconstruction problem. PS and low tensor multi-rank constrains are jointly enforced to reconstruct dynamic MRI data. We develop an efficient algorithm based on the alternating direction method of multipliers (ADMM) to solve the proposed optimization problem. The experimental results demonstrate the superior performance of the proposed method.

KW - Dynamic magnetic imaging

KW - Low rank tensor

KW - Partial separability

KW - Tensor singular value decompositoin

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BT - Proceedings of 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology, ICBCB 2019

PB - Institute of Electrical and Electronics Engineers Inc.

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Ma S, Du H , Wu Q, Mei W. Dynamic MRI reconstruction exploiting partial separability and t-SVD. In Proceedings of 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology, ICBCB 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 179-184. 8854641. (Proceedings of 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology, ICBCB 2019). doi: 10.1109/ICBCB.2019.8854641

Dynamic MRI reconstruction exploiting partial separability and t-SVD

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