Motion-compensated data decomposition algorithm to accelerate dynamic cardiac MRI
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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.
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.
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.
KeywordsCompressed sensing Low-rank matrix completion Motion compensation Cardiac MRI
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.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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.
- 3.Lustig M, Santos JM, Donoho DL, Pauly JM (2006) k-t SPARSE: high frame rate dynamic MRI exploiting spatio-temporal sparsity. In: Proceedings of the 13th annual meeting of international society for magnetic resonance in medicine (ISMRM), USA, p 2420Google Scholar
- 12.Liang ZP (2007) Spatiotemporal imaging with partially separable functions. In: Proceedings of IEEE international symposium biomedical imaging, pp 988–991Google Scholar
- 13.Haldar J, Liang ZP (2010) Spatiotemporal imaging with partially separable functions: a matrix recovery approach. In: Proceedings of IEEE international symposium biomedical imaging, pp 716–719Google Scholar
- 14.Lustig M, Elad M, Pauly J (2010) Calibrationless parallel imaging reconstruction by structured low-rank matrix completion. In: Proceedings of the 18th annual meeting of international society for magnetic resonance in medicine (ISMRM), p 2870Google Scholar
- 19.Gao H, Rapacchi S, Wang D, Moriarty J, Meehan C, Sayre J, Laub G, Finn P, Hu P (2012) Compressed sensing using prior rank, intensity and sparsity model (PRISM): applications in cardiac cine MRI. In: Proceedings of the 20th annual meeting of international society for magnetic resonance in medicine (ISMRM), p 2242Google Scholar
- 24.Royuela-del-Val J, Cordero-Grande L, Simmross-Wattenberg F, Martín-Fernández M, Alberola-López C (2016) Nonrigid groupwise registration for motion estimation and compensation in compressed sensing reconstruction of breath-hold cardiac cine MRI. Magn Reson Med 75:1525–1536CrossRefPubMedGoogle Scholar
- 25.Sharif B, Bresler Y (2007) Physiologically improved NCAT phantom (PINCAT) enables in silico study of the effects of beat-to-beat variability on cardiac MR. In: Proceedings of international society for magnetic resonance in medicine (ISMRM), p 3418Google Scholar
- 28.Dowling J, Bourgeat P, Raffelt D, Fripp J, Greer PB, Patterson J, Denham J, Gupta S, Tang C, Stanwell P, Ourselin S, Salvado O (2009) Non-rigid correction of interleaving artefacts in pelvic MRI. In: Proceedings of SPIE medical imaging 2009: image processing, vol 7259Google Scholar