Reducing clustering of readouts in non-Cartesian cine magnetic resonance imaging.

Abstract

Background: Non-Cartesian magnetic resonance imaging trajectories at golden angle increments have the advantage of allowing motion correction and gating using intermediate real-time reconstructions. However, when the acquired data are cardiac binned for cine imaging, trajectories can cluster together at certain heart rates (HR) causing image artifacts. Here, we demonstrate an approach to reduce clustering by inserting additional angular increments within the trajectory, and optimizing them while still allowing for intermediate reconstructions.

Methods: Three acquisition models were simulated under constant and variable HR: golden angle (M_trd), random additional angles (M_rnd), and optimized additional angles (M_opt). The standard deviations of trajectory angular differences (STAD) were compared through their interquartile ranges (IQR) and the Kolmogorov-Smirnov test (significance level: p = 0.05). Agreement between an image reconstructed with uniform sampling and images from M_trd, M_rnd, and M_opt was analyzed using the structural similarity index measure (SSIM). M_trd and M_opt were compared in three adults at high, low, and no HR variability.

Results: STADs from M_trd were significantly different (p < 0.05) from M_opt and M_rnd. STAD (IQR × 10^-2 rad) showed that M_opt (0.5) and M_rnd (0.5) reduced clustering relative to M_trd (1.9) at constant HR. For variable HR, M_opt (0.5) and M_rnd (0.5) outperformed M_trd (0.9). The SSIM (IQR) showed that M_opt (0.011) produced the best image quality, followed by M_rnd (0.014), and M_trd (0.030). M_opt outperformed M_trd at reduced HR variability in in-vivo studies. At high HR variability, both models performed well.

Conclusion: This approach reduces clustering in k-space and improves image quality.