Keywords: Parallel Transmit & Multiband, Parallel Transmit & Multiband
Motivation: Optimization of parallel transmission (pTx) pulse design with hard constraints on SAR will benefit from faster approaches.
Goal(s): We seek to incorporate hard quadratic constraints for pTx using a physics-driven deep learning (DL) approach.
Approach: We unroll an extension of the log-barrier method to enforce SAR constraints, while learning the optimal gradient step sizes using a neural network. This strategy accelerates optimization with fewer steps, while not sacrificing performance.
Results: Preliminary results show that our method is faster than traditional techniques like CVXPY with similar performance.
Impact: Our proposed method reduces the time-consuming optimization used in conventional pTx and may lead to improvements especially for real-time UHF applications.
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