Accelerated dynamic MRI reconstruction with total variation and nuclear norm regularization

Jiawen Yao, Zheng Xu, Xiaolei Huang, Junzhou Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Citations (Scopus)

Abstract

In this paper, we propose a novel compressive sensing model for dynamic MR reconstruction. With total variation (TV) and nuclear norm (NN) regularization, our method can utilize both spatial and temporal redundancy in dynamic MR images. Due to the non-smoothness and non-separability of TV and NN terms, it is difficult to optimize the primal problem. To address this issue, we propose a fast algorithm by solving a primal-dual form of the original problem. The ergodic convergence rate of the proposed method is O(1/N) for N iterations. In comparison with six state-of-the-art methods, extensive experiments on single-coil and multi-coil dynamic MR data demonstrate the superior performance of the proposed method in terms of both reconstruction accuracy and time complexity.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings
EditorsJoachim Hornegger, Alejandro F. Frangi, William M. Wells, Alejandro F. Frangi, Nassir Navab, Joachim Hornegger, Nassir Navab, William M. Wells, William M. Wells, Alejandro F. Frangi, Joachim Hornegger, Nassir Navab
PublisherSpringer Verlag
Pages635-642
Number of pages8
ISBN (Print)9783319245706, 9783319245706, 9783319245706
DOIs
StatePublished - Jan 1 2015
Event18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: Oct 5 2015Oct 9 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9350
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
CountryGermany
CityMunich
Period10/5/1510/9/15

Fingerprint

Total Variation
Magnetic resonance imaging
Regularization
Coil
Norm
Compressive Sensing
Primal-dual
Regularization Method
Fast Algorithm
Time Complexity
Redundancy
Rate of Convergence
Optimise
Iteration
Term
Demonstrate
Experiment
Experiments
Model

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Yao, J., Xu, Z., Huang, X., & Huang, J. (2015). Accelerated dynamic MRI reconstruction with total variation and nuclear norm regularization. In J. Hornegger, A. F. Frangi, W. M. Wells, A. F. Frangi, N. Navab, J. Hornegger, N. Navab, W. M. Wells, W. M. Wells, A. F. Frangi, J. Hornegger, ... N. Navab (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings (pp. 635-642). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9350). Springer Verlag. https://doi.org/10.1007/978-3-319-24571-3_76
Yao, Jiawen ; Xu, Zheng ; Huang, Xiaolei ; Huang, Junzhou. / Accelerated dynamic MRI reconstruction with total variation and nuclear norm regularization. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings. editor / Joachim Hornegger ; Alejandro F. Frangi ; William M. Wells ; Alejandro F. Frangi ; Nassir Navab ; Joachim Hornegger ; Nassir Navab ; William M. Wells ; William M. Wells ; Alejandro F. Frangi ; Joachim Hornegger ; Nassir Navab. Springer Verlag, 2015. pp. 635-642 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "In this paper, we propose a novel compressive sensing model for dynamic MR reconstruction. With total variation (TV) and nuclear norm (NN) regularization, our method can utilize both spatial and temporal redundancy in dynamic MR images. Due to the non-smoothness and non-separability of TV and NN terms, it is difficult to optimize the primal problem. To address this issue, we propose a fast algorithm by solving a primal-dual form of the original problem. The ergodic convergence rate of the proposed method is O(1/N) for N iterations. In comparison with six state-of-the-art methods, extensive experiments on single-coil and multi-coil dynamic MR data demonstrate the superior performance of the proposed method in terms of both reconstruction accuracy and time complexity.",
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Yao, J, Xu, Z, Huang, X & Huang, J 2015, Accelerated dynamic MRI reconstruction with total variation and nuclear norm regularization. in J Hornegger, AF Frangi, WM Wells, AF Frangi, N Navab, J Hornegger, N Navab, WM Wells, WM Wells, AF Frangi, J Hornegger & N Navab (eds), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9350, Springer Verlag, pp. 635-642, 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015, Munich, Germany, 10/5/15. https://doi.org/10.1007/978-3-319-24571-3_76

Accelerated dynamic MRI reconstruction with total variation and nuclear norm regularization. / Yao, Jiawen; Xu, Zheng; Huang, Xiaolei; Huang, Junzhou.

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings. ed. / Joachim Hornegger; Alejandro F. Frangi; William M. Wells; Alejandro F. Frangi; Nassir Navab; Joachim Hornegger; Nassir Navab; William M. Wells; William M. Wells; Alejandro F. Frangi; Joachim Hornegger; Nassir Navab. Springer Verlag, 2015. p. 635-642 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9350).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - In this paper, we propose a novel compressive sensing model for dynamic MR reconstruction. With total variation (TV) and nuclear norm (NN) regularization, our method can utilize both spatial and temporal redundancy in dynamic MR images. Due to the non-smoothness and non-separability of TV and NN terms, it is difficult to optimize the primal problem. To address this issue, we propose a fast algorithm by solving a primal-dual form of the original problem. The ergodic convergence rate of the proposed method is O(1/N) for N iterations. In comparison with six state-of-the-art methods, extensive experiments on single-coil and multi-coil dynamic MR data demonstrate the superior performance of the proposed method in terms of both reconstruction accuracy and time complexity.

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PB - Springer Verlag

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Yao J, Xu Z, Huang X, Huang J. Accelerated dynamic MRI reconstruction with total variation and nuclear norm regularization. In Hornegger J, Frangi AF, Wells WM, Frangi AF, Navab N, Hornegger J, Navab N, Wells WM, Wells WM, Frangi AF, Hornegger J, Navab N, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings. Springer Verlag. 2015. p. 635-642. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-24571-3_76