On Density Compensation in Bayesian K-Space Trajectory Optimization

Jan Aelterman¹, Hiep Luong¹, Bart Goossens¹, Aleksandra Pizurica¹, Wilfried Philips¹

It is demonstrated that trajectory optimization is of crucial importance in compressed sensing MRI reconstruction. Variational Bayesian k-space trajectory optimization techniques is then analyzed from the point of view of variable density k-space sampling. It is shown that density compensation is imperative in this framework and we present new and computationally very efficient method to estimate the density compensation function.