Optimizing Teardrop, an MRI sampling trajectory

Teardrop is an efficient sampling trajectory for acquiring magnetic resonance imaging Data, especially balanced steady state free precession images. In this paper, we present two models for optimizing such trajectories. These are the first models to incorporate motion-insensitivity constraints into a nonraster (also called spiral) sampling trajectory. The first model is nonlinear and very specific to Teardrop. The second model uses sequential second-order cone programming, and is generalizable to other trajectories in two and three dimensions. We present a weak convergence proof for the sequential method.