SelExNet: A Self-Supervised Physics-Informed Framework for Multi-Channel Joint RF and Gradient Waveform Optimization in 2D Spatially Selective Excitation.

purposeTo introduce SelExNet: a self-supervised framework for two-dimensional spatially selective excitation that jointly optimizes radiofrequency (RF) pulses and gradient waveforms, and extends to multi-channel transmission MRI.

methodsBuilding on prior RF-only and joint RF-gradient optimization approaches, SelExNet couples neural RF and gradient generators with a differentiable Bloch simulator to enable self-supervised pulse optimization without requiring pre-designed target pulses. The framework jointly designs RF pulses and parameterized variable-density spiral gradient waveforms for both single- and multi-channel transmission, with patient-specific adaptation using measured, previously unseen

resultsJoint optimization of RF and gradients improved excitation fidelity compared to RF-only optimization. In phantom experiments with synthetic field maps, pretrained pulses showed distortions, whereas fine-tuned pulses restored geometry and uniformity. In vivo studies demonstrated anatomically precise excitation, with fine-tuning improving sharpness and reducing off-target signal.

conclusionThe proposed framework enables joint RF-gradient design and extends self-supervised pulse optimization to multi-channel transmission MRI. SelExNet achieves high-fidelity, anatomically precise excitation and demonstrates robustness to field inhomogeneities, offering a scalable pathway for ultra-high field imaging.