Accelerated Cardiac Cine MRI Using Locally Low Rank and Finite Difference Constraints

Xin Miao, Sajan Goud Lingala, Yi Guo, Terrence Jao, Muhammad Usman, Claudia Prieto, Krishna S. Nayak

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)
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Abstract

Purpose To evaluate the potential value of combining multiple constraints for highly accelerated cardiac cine MRI. Methods A locally low rank (LLR) constraint and a temporal finite difference (FD) constraint were combined to reconstruct cardiac cine data from highly undersampled measurements. Retrospectively undersampled 2D Cartesian reconstructions were quantitatively evaluated against fully-sampled data using normalized root mean square error, structural similarity index (SSIM) and high frequency error norm (HFEN). This method was also applied to 2D golden-angle radial real-time imaging to facilitate single breath-hold whole-heart cine (12 short-axis slices, 9–13 sec single breath hold). Reconstruction was compared against state-of-the-art constrained reconstruction methods: LLR, FD, k-t SLR. Results At 10 to 60 spokes/frame, LLR + FD better preserved fine structures and depicted myocardial motion with reduced spatio-temporal blurring in comparison to existing methods. LLR yielded higher SSIM ranking than FD; FD had higher HFEN ranking than LLR. LLR + FD combined the complimentary advantages of the two, and ranked the highest in all metrics for all retrospective undersampled cases. Single breath-hold multi-slice cardiac cine with prospective undersampling was enabled with in-plane spatio-temporal resolutions of 2 × 2 mm2 and 40 ms. Conclusion Highly accelerated cardiac cine is enabled by the combination of 2D undersampling and the synergistic use of LLR and FD constraints.
Original languageEnglish
JournalMagnetic resonance imaging
Volume34
Issue number6
Early online date8 Mar 2016
DOIs
Publication statusE-pub ahead of print - 8 Mar 2016

Keywords

  • Cardiac cine MRI
  • sparse sampling
  • constrained reconstruction
  • locally low rank
  • compressed sensing
  • parallel imaging

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