FastPtx: a versatile toolbox for rapid, joint design of pTx RF and gradient pulses using Pytorch’s autodifferentiation

Objective With modern optimization methods, free optimization of parallel transmit pulses together with their gradient waveforms can be performed on-line within a short time. A toolbox which uses PyTorch’s autodifferentiation for simultaneous optimization of RF and gradient waveforms is presented and its performance is evaluated. Methods MR measurements were performed on a 9.4T MRI scanner using a 3D saturated single-shot turboFlash sequence for B_1^+ mapping. RF pulse simulation and optimization were done using a Python toolbox and a dedicated server. An RF- and Gradient pulse design toolbox was developed, including a cost function to balance different metrics and respect hardware and regulatory limits. Pulse performance was evaluated in GRE and MPRAGE imaging. Pulses for non-selective and for slab-selective excitation were designed. Results Universal pulses for non-selective excitation reduced the flip angle error to an NRMSE of (12.3±1.7)