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)% relative to the targeted flip angle in simulations, compared to (42.0±1.4)% in CP mode. The tailored pulses performed best, resulting in a narrow flip angle distribution with NRMSE of (8.2±1.0)%. The tailored pulses could be created in only 66 s, making it feasible to design them during an experiment. A 90° pulse was designed as preparation pulse for a satTFL sequence and achieved a NRMSE of 7.1%. We showed that both MPRAGE and GRE imaging benefited from the pTx pulses created with our toolbox.

Conclusion

The pTx pulse design toolbox can freely optimize gradient and pTx RF waveforms in a short time. This allows for tailoring high-quality pulses in just over a minute.