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Abstract #4425

Optimizing Radiofrequency Pulses using Deep Learning Frameworks

Tristhal Parasram1, Jacob Bondy1, and Xiao Dan1
1Physics, University of Windsor, Windsor, ON, Canada

Synopsis

Keywords: RF Pulse Design & Fields, RF Pulse Design & Fields, Deep learning; MESE

Motivation: Utilize modern deep learning techniques to efficiently generate RF pulses on a GPU for specific applications.

Goal(s): Develop a fast, easy-to-use framework to optimize RF pulses and demonstrate its effectiveness by generating slice-selective 90° excitation and 180° plane refocusing pulses for MESE experiments.

Approach: RF pulses were optimized using neural network frameworks by training to achieve a target profile on a set of simulated phantoms, in a process that mirrors neural network training.

Results: The optimized pulses outperformed the SLR pulses in MESE experiments on both phantom and mouse brain.

Impact: A neural network framework was developed to create high-performance RF pulses that lead to improved image quality. Constraints such as application-specific considerations and hardware limitations or perturbations can be easily incorporated into the framework for fast, easy-to-use RF pulse generation.

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Keywords