The RF safety assessment of implants is a computationally demanding task. An acceleration method was presented in [1] where the local field enhancement was determined by sparse matrix inversion. In this work, we show how a model-based deep learning approach for unrolled optimization could significantly reduce the number of iterations required. The benefit of this approach is that traditional minimization is still possible afterwards, combining short computation times with high accuracy. We trained 5 iterations with 10.000 randomly generated implants. The hybrid approach finds a numerically equivalent solution in$$$\,\frac{1}{13}^{th}\,$$$of the traditional method. This approach would enable online RF safety assessment.
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