Towards Fast Hard-Constrained Parallel Transmit Design in Ultrahigh Field MRI with Physics-Driven Neural Networks - Kilic, Toygan; Herrler, Jurgen; Liebig, Patrick; Demirel, Omer Burak; Nagel, Armin Michael; Hong, Mingyi; Giannakis, Georgios B.; Ugurbil, Kamil; Akcakaya, Mehmet (Proceedings - International Symposium on Biomedical Imaging, 2024)
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.
CRIS-Team Scopus Importer:2024-09-20
Keywords: hard constraints; log-barrier; neural networks; optimization; parallel transmit; ultrahigh field MRI