Improving spreading projection algorithm for rapid k-space sampling trajectories through minimized off-resonance effects and gridding of low frequencies
Chaithya Giliyar Radhakrishna, Guillaume Daval-Frérot, Aurélien Massire, Alexandre Vignaud, Philippe Ciuciu
Abstract
Purpose
Non-Cartesian MRI with long arbitrary readout directions are susceptible to off-resonance artifacts due to patient induced B0 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.
Theory
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.
Methods
Prospective k-space data was acquired at 3 T on new trajectories, and we show robustness to B0 inhomogeneities through in silico experiments by adding ΔB0 through artificial degradation of system B0 shimming. Later on, in vivo experiments were carried out to optimize parameters of the new improvements and benchmark the gain in performance.
Results
The improved trajectories allowed for the recovery of signal dropouts observed on original SPARKLING acquisitions at larger B0 field inhomogeneities. Furthermore, imposing gridded sampling at the center of k-space provided improved reconstructed image quality with limited artifacts.