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

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ABSTRACT Purpose To 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. Methods Building 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 and maps. Results Joint 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. Conclusion The 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.

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