Towards Fast Hard-Constrained Parallel Transmit Design in Ultrahigh Field MRI with Physics-Driven Neural Networks
Kilic T, Herrler J, Liebig P, Demirel OB, Nagel AM, Hong M, Giannakis GB, Ugurbil K, Akcakaya M (2024)
Publication Type: Conference contribution
Publication year: 2024
Journal
Publisher: IEEE Computer Society
Conference Proceedings Title: Proceedings - International Symposium on Biomedical Imaging
Event location: Athens
ISBN: 9798350313338
DOI: 10.1109/ISBI56570.2024.10635855
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.
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APA:
Kilic, T., Herrler, J., Liebig, P., Demirel, O.B., Nagel, A.M., Hong, M.,... Akcakaya, M. (2024). Towards Fast Hard-Constrained Parallel Transmit Design in Ultrahigh Field MRI with Physics-Driven Neural Networks. In Proceedings - International Symposium on Biomedical Imaging. Athens, GR: IEEE Computer Society.
MLA:
Kilic, Toygan, et al. "Towards Fast Hard-Constrained Parallel Transmit Design in Ultrahigh Field MRI with Physics-Driven Neural Networks." Proceedings of the 21st IEEE International Symposium on Biomedical Imaging, ISBI 2024, Athens IEEE Computer Society, 2024.
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