Laterally Oscillating Trajectory for Undersampling Slices: LOTUS

Abstract

Purpose: While spiral sampling offers SNR advantages for diffusion MRI, its acceleration with simultaneous multislice remains relatively unexplored. This study introduces Laterally Oscillating Trajectory for Undersampling Slices (LOTUS), which is a 3D spiral-like k-space trajectory that aims to minimize g-factor via controlled incoherent aliasing. To aid in validation, we also introduce a robust method to estimate g-factor for iterative non-Cartesian reconstructions. Methods: Simulated data sampling of a numerical phantom was performed using LOTUS and several acquisition schemes proposed by others to quantitatively compare the resulting image quality when compared to a known ground truth. Diffusion-weighted in vivo brain data from two subjects was acquired with two in-plane acceleration factors (2x and 4x), and two slice acceleration factors (2x and 4x). Estimated g-factor maps and fractional anisotropy maps were calculated to quantitatively and qualitatively compare trajectory performance. For both simulation and in vivo, reconstructions both with and without compressed sensing were utilized. Results: Simulations generally showed decreased g-factor (20%-31%, depending on trajectory, at highest undersampling rate) and improved reconstruction accuracy (mean-square error, structural similarity index, and entropy metrics) for LOTUS compared to the other trajectories. The in vivo acquisitions demonstrated g-factor benefits and qualitative image quality improvements that mirrored the simulation results. For both simulation and in vivo, improvements for LOTUS increased for higher numbers of simultaneous slices. Conclusion: By enabling higher rates of slice acceleration, LOTUS shows promise for decreasing scan time, which is especially beneficial for diffusion MRI.