Clipped DeepControl:Deep neural network two-dimensional pulse design with an amplitude constraint layer
| Autor: | Mads Sloth Vinding, Torben Ellegaard Lund |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Phantoms Imaging Radio Waves Medicine (miscellaneous) FOS: Physical sciences Systems and Control (eess.SY) Physics - Medical Physics Electrical Engineering and Systems Science - Systems and Control Machine Learning (cs.LG) Magnetic Resonance Imaging/methods Artificial Intelligence Heart Rate FOS: Electrical engineering electronic engineering information engineering Medical Physics (physics.med-ph) Neural Networks Computer |
| Zdroj: | Vinding, M S & Lund, T E 2023, ' Clipped DeepControl : Deep neural network two-dimensional pulse design with an amplitude constraint layer ', Artificial Intelligence in Medicine, vol. 135, 102460 . https://doi.org/10.1016/j.artmed.2022.102460 |
| Popis: | Advanced radio-frequency pulse design used in magnetic resonance imaging has recently been demonstrated with deep learning of (convolutional) neural networks and reinforcement learning. For two-dimensionally selective radio-frequency pulses, the (convolutional) neural network pulse prediction time (a few milliseconds) was in comparison more than three orders of magnitude faster than the conventional optimal control computation. The network pulses were from the supervised training capable of compensating scan-subject dependent inhomogeneities of B 0 and B 1 + fields. Unfortunately, the network presented with a small percentage of pulse amplitude overshoots in the test subset, despite the optimal control pulses used in training were fully constrained. Here, we have extended the convolutional neural network with a custom-made clipping layer that completely eliminates the risk of pulse amplitude overshoots, while preserving the ability to compensate for the inhomogeneous field conditions. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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