Stochastic optimization of 3D non-Cartesian sampling trajectory (SNOPY)

Efficient k-space trajectories are crucial for accelerated MRI. SNOPY proposes a generalized gradient-based method for optimizing 3D non-Cartesian sampling patterns. The algorithm can simultaneously tune multiple properties of sampling patterns, including image quality, hardware constraints (maximum slew rate and gradient strength), reduced peripheral nerve stimulation (PNS), and parameter-weighted contrast. The proposed method applies to various scenarios, such as optimizing gradient waveforms or optimizing rotation angles of radial/spiral trajectories. We adopted several computational strategies to address this non-convex and large-scale problem. Various simulated and in-vivo experiments demonstrated the effectiveness of SNOPY.