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

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), random additional angles (M), and optimized additional angles (M). 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, M, and M was analyzed using the structural similarity index measure (SSIM). M and M were compared in three adults at high, low, and no HR variability.

Results: STADs from M were significantly different (p < 0.05) from M and M. STAD (IQR × 10 rad) showed that M (0.5) and M (0.5) reduced clustering relative to M (1.9) at constant HR. For variable HR, M (0.5) and M (0.5) outperformed M (0.9). The SSIM (IQR) showed that M (0.011) produced the best image quality, followed by M (0.014), and M (0.030). M outperformed M 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.