Improving spreading projection algorithm for rapid k-space sampling trajectories through minimized off-resonance...

Non-Cartesian MRI with long arbitrary readout directions are susceptible to off-resonance artifacts due to patient induced $$ inhomogeneities. This results in degraded image quality with strong signal losses and blurring. Current solutions to address this issue involve correcting the off-resonance artifacts during image reconstruction or reducing inhomogeneities through improved shimming.

The recently developed SPARKLING algorithm is extended to drastically diminish off-resonance artifacts by generating temporally smooth k-space sampling patterns. For doing so, the cost function which is optimized in SPARKLING is modified using a temporal weighting factor. Additionally, oversampling of the center of k-space beyond the Nyquist criteria is prevented through the use of gridded sampling in the region, enforced with affine constraints.

Prospective k-space data was acquired at 3 T on new trajectories, and we show robustness to $$ inhomogeneities through in silico experiments by adding $$  through artificial degradation of system $$ shimming. Later on, in vivo experiments were carried out to optimize parameters of the new improvements and benchmark the gain in performance.

The improved trajectories allowed for the recovery of signal dropouts observed on original SPARKLING acquisitions at larger $$ field inhomogeneities. Furthermore, imposing gridded sampling at the center of k-space provided improved reconstructed image quality with limited artifacts.

These advancements allowed us for nearly $$ shorter scan time compared to GRAPPA-p4x1, allowing us to reach 600 µm isotropic resolution in 3D $$ -w imaging in just 3.3 min at 3 T with negligible degradation in image quality.