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Motion-compensated data decomposition algorithm to accelerate dynamic cardiac MRI

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Magnetic Resonance Materials in Physics, Biology and Medicine Aims and scope Submit manuscript

Abstract

Objectives

In dynamic cardiac magnetic resonance imaging (MRI), the spatiotemporal resolution is often limited by low imaging speed. Compressed sensing (CS) theory can be applied to improve imaging speed and spatiotemporal resolution. The combination of compressed sensing and low-rank matrix completion represents an attractive means to further increase imaging speed. By extending prior work, a Motion-Compensated Data Decomposition (MCDD) algorithm is proposed to improve the performance of CS for accelerated dynamic cardiac MRI.

Materials and methods

The process of MCDD can be described as follows: first, we decompose the dynamic images into a low-rank (L) and a sparse component (S). The L component includes periodic motion in the background, since it is highly correlated among frames, and the S component corresponds to respiratory motion. A motion-estimation/motion-compensation (ME-MC) algorithm is then applied to the low-rank component to reconstruct a cardiac motion compensated dynamic cardiac MRI.

Results

With validations on the numerical phantom and in vivo cardiac MRI data, we demonstrate the utility of the proposed scheme in significantly improving compressed sensing reconstructions by minimizing motion artifacts. The proposed method achieves higher PSNR and lower MSE and HFEN for medium to high acceleration factors.

Conclusion

The proposed method is observed to yield reconstructions with minimal spatiotemporal blurring and motion artifacts in comparison to the existing state-of-the-art methods.

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Notes

  1. http://cai2r.net/research/ls-reconstruction, https://research.engineering.uiowa.edu/cbig/content/software.

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Acknowledgements

The authors would like to thank Dr. Jong Ye for making the dynamic cardiac data available online: (http://bisp.kaist.ac.kr/ktFOCUSS.htm). This research has been supported by NSERC Discovery Grant RGPIN/239007.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, M5B2K3, Canada

    Azar Tolouee & Javad Alirezaie

  2. Department of Medical Imaging, University of Saskatchewan and Saskatoon Health Region, Saskatoon, SK, Canada

    Paul Babyn

Authors
  1. Azar Tolouee
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  2. Javad Alirezaie
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  3. Paul Babyn
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Corresponding author

Correspondence to Javad Alirezaie.

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The authors declare that they have no conflict of interest.

Ethical standards

In this study, we used dynamic cardiac data available online (http://bisp.kaist.ac.kr/ktFOCUSS.htm). The Institutional Review Board of the University of Southern California approved the imaging protocols. Each subject was screened for magnetic resonance imaging risk factors and provided informed consent in accordance with institutional policy.

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Tolouee, A., Alirezaie, J. & Babyn, P. Motion-compensated data decomposition algorithm to accelerate dynamic cardiac MRI. Magn Reson Mater Phy 31, 33–47 (2018). https://doi.org/10.1007/s10334-017-0628-x

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  • DOI: https://doi.org/10.1007/s10334-017-0628-x

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