Jan Aelterman1, Hiep Luong1, Bart Goossens1, Aleksandra Pizurica1, Wilfried Philips1
1TELIN-IPI-IBBT, Ghent University, Ghent, Belgium
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.