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Deep Reinforcement Learning Designed Shinnar-Le Roux RF Pulse Using Root-Flipping: DeepRFSLR.
IEEE Transactions on Medical Imaging
|August 25, 2020
Summary
Deep reinforcement learning optimizes radiofrequency (RF) pulse design for faster, shorter multiband pulses. This machine learning approach significantly reduces computational time and pulse duration in magnetic resonance imaging (MRI).
Area of Science:
- Magnetic Resonance Imaging (MRI)
- Machine Learning
- Signal Processing
Background:
- Designing radiofrequency (RF) pulses for multiband imaging is computationally intensive.
- Conventional methods for Shinar Le-Roux (SLR) pulse design can be time-consuming and may not achieve optimal pulse durations.
Purpose of the Study:
- To introduce a novel method, DeepRFSLR, for optimizing RF pulse design using deep reinforcement learning.
- To minimize peak amplitude or pulse duration of multiband refocusing pulses generated by the SLR algorithm.
Main Methods:
- Optimization of the SLR polynomial root pattern using iterative deep reinforcement learning and greedy tree search.
- Application of the DeepRFSLR method to design multiband pulses for three and seven slices.
Main Results:
- DeepRFSLR generated shorter duration RF pulses compared to conventional methods.
- The method achieved improved performance in significantly shorter computational time.
- RF pulses produced by DeepRFSLR demonstrated slice profiles similar to minimum-phase SLR pulses and matched simulations.
Conclusions:
- DeepRFSLR offers an efficient and effective approach for designing "machine-designed" MRI sequences.
- The application of machine learning algorithms can advance RF pulse design in MRI.
- This method holds promise for accelerating MRI acquisition and improving image quality.
