Towards Fast Hard-Constrained Parallel Transmit Design in Ultrahigh Field MRI with Physics-Driven Neural Networks

Toygan Kilic, Jürgen Herrler, Patrick Liebig, Ömer Burak Demirel, Armin M. Nagel, Mingyi Hong, Georgios B. Giannakis, Kâmil Ugurbil, Mehmet Akçakaya

Abstract: Parallel transmission (pTx) is an important technique for reducing transmit field inhomogeneities at ultrahigh-field (UHF) MRI. pTx typically involves solving an optimization problem for radiofrequency pulse design, with hard constraints on specific-absorption rate (SAR) and/or power, which may be time-consuming. In this work, we propose a novel approach towards incorporating hard constraints to physics-driven neural networks. Our method unrolls an extension of the log-barrier method, where the central path problems are solved via the gradient descent method whose optimal step sizes are learned with a neural network. Results indicate that our method is substantially faster compared to traditional convex optimization techniques, while achieving similar performance.