Stochastic methods code#

MRI data-driven optimization deep learning image acquisition non-Cartesian sampling. The algorithm can simultaneously optimize 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. SNOPY provides an efficient data-driven and optimization-based method to tailor non-Cartesian sampling trajectories. Optimizing the gradient waveform of a rotational EPI trajectory improved participants' rating of the PNS from 'strong' to 'mild.' In the prospective studies, by optimizing the rotation angles of a stack-of-stars (SOS) trajectory, SNOPY reduced the NRMSE of reconstructed images from 1.19 to 0.97 compared to the best empirical method (RSOS-GR). In the simulation studies, the image quality of a 3D kooshball trajectory was improved from 0.29 to 0.22 (NRMSE) with Stochastic optimization framework for 3D NOn-Cartesian samPling trajectorY (SNOPY) optimization. We applied the optimized trajectory to multiple applications including structural and functional imaging.

Stochastic methods code#

The corresponding code is available as an open-source toolbox. We proposed several strategies to alleviate the severe nonconvexity and huge computation demand posed by the large scale. Notably, the method can optimize sampling trajectories synergistically with either model-based or learning-based reconstruction methods. The proposed method can either optimize the gradient waveform (spline-based freeform optimization) or optimize properties of given sampling trajectories (such as the rotation angle of radial trajectories). The algorithm can simultaneously optimize 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. We built a differentiable simulation model to enable gradient-based methods for sampling trajectory optimization. This work proposes a generalized framework for optimizing 3D non-Cartesian sampling patterns via data-driven optimization. Optimizing three-dimensional (3D) k-space sampling trajectories is important for efficient MRI yet presents a challenging computational problem.