Durga: A heuristically-optimized data collection strategy for volumetric magnetic resonance imaging

A heuristic design method for rapid volumetric magnetic resonance imaging data acquisition trajec-tories is presented, using a series of second order cone optimization subproblems. Other researchers have considered non-raster data collection trajectories and under-sampled data patterns. This work demonstrates that much higher rates of under-sampling are possible with an asymmetric set of tra-jectories, with very little loss in resolution, but the addition of noise-like artifacts. The proposed data collection trajectory, Durga, further minimizes collection time by incorporating short, un-refocussed excitation pulses, resulting in above 98 percent collection efficiency for balanced steady state free precession imaging. The optimization subproblems are novel, in that they incorporate all requirements , including data collection (coverage), physicality (device limits) and signal generation (zeroth and higher moment properties) in a single convex problem, which allows the resulting trajectories to exhibit a higher collection efficiency than any existing trajectory design.