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Abstract #3803

Evaluating and Predicting Non-linear Acoustics Using Multi-Axis Gradient Variations and Convolutional Neural Networks

Brock Jolicoeur1, Andrew Alexander2,3,4, Steven Kecskemeti2, and Kevin Johnson3,5
1Biomedical Engineering, Univeristy of Wisconsin-Madison, Madison, WI, United States, 2Waisman Center, Univeristy of Wisconsin-Madison, Madison, WI, United States, 3Medical Physics, Univeristy of Wisconsin-Madison, Madison, WI, United States, 4Psychiatry, University of Wisconsin-Madison, Madison, WI, United States, 5Radiology, University of Wisconsin-Madison, Madison, WI, United States

Synopsis

Keywords: Bioeffects & Magnetic Fields, Bioeffects & Magnetic Fields, Acoustic Noise

Motivation: MRI scanners produce substantial acoustic noise that may affect sensitive groups. Traditional acoustic models, assuming linear sound pressure level (SPL) scale and combine linearly along three separate axes, may oversimplify system dynamics.

Goal(s): To evaluate SPL linearity and develop a predictive model that improves accuracy over traditional methods.

Approach: We implemented a custom pulse sequence design to test gradient amplitudes and frequencies and used a convolutional neural network(CNN) to predict SPL changes on an ultra-high performance head-only system.

Results: Non-linear SPL responses were observed; our CNN more accurately predicted SPL compared to linear models, demonstrating potential for improved patient comfort and image quality.

Impact: This study challenges existing linear acoustic models in MRI, offering a more complex and accurate understanding of SPL dynamics. With improved acoustic noise estimation, more informed choices can be made when developing pulse sequences with reduced SPL.

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Keywords