Optimal variable-density k-space sampling in MRI

Magnetic resonance imaging (MRI) is often times limited by scan time. To reduce scan time, there have been various efforts to reduce the number of sampling points. In most cases, this is done by utilizing a priori knowledge of the signal of interest. In this paper, we propose an algorithm that will guide the determination of an optimal sampling pattern based on prior knowledge of the signal. Applications of this method include optimal variable-density k-space trajectory design or reduced phase encoding in 3DFT and spectroscopy. Preliminary results are shown as a proof of concept.