Optimization of Gradient Pulse Shape Prediction Using Temporal Convolutional Networks

Purpose To improve TCN‐based gradient‐waveform prediction with respect to acquisition efficiency and inference speed, and to evaluate its use for radial imaging. Methods Gradient waveforms were measured using a thin‐slice method on a preclinical MRI system. Separate TCN models were trained for each physical gradient axis to predict both gradient and terms. The framework was evaluated using phantom and in vivo radial acquisitions across a range of receiver bandwidths and compared with measured trajectories and gradient impulse response function (GIRF)‐based trajectory predictions. Results The proposed workflow reduced model complexity and substantially improved prediction speed. In phantom and in vivo radial imaging, model‐based trajectories provided better image quality over a wider range of receiver bandwidths than GIRF‐based trajectories. term estimation provided limited additional visual benefit after scanner preemphasis adjustment. Conclusion TCN‐based prediction of gradient‐waveform distortions can be made more efficient while remaining effective for radial MRI trajectory estimation on the studied preclinical system, particularly in demanding high‐bandwidth acquisitions.