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Abstract #5118

Proved feasibility of rapid dielectric shimming prediction in MRI using Convolutional Neural Network on enriched dataset

Mengying Zhang1,2, Nazish Murad1, Fraser Robb3, and Simone Winkler1,2,4
1Department of Radiology, Weill Cornell Medical College, New York, NY, United States, 2Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, United States, 3GE Healthcare, Aurora, OH, United States, 4Department of Biomedical Engineering, Cornell University, Ithaca, NY, United States

Synopsis

Keywords: Diagnosis/Prediction, AI/ML Software

Motivation: Inhomogeneities of the MRI transmit field causes image shading and hinders diagnosis. Implementing dielectric shimming, high-permittivity pads are used to recover signal in low-intensity areas, but full-wave calculation of the resulting fields is slow for real-time use at the scanner and requires massive computation.

Goal(s): To prove the feasibility of CNN on rapid prediction of the transmit field with dielectric pads.

Approach: U-Net architecture is trained on an enriched simulated data with diversified human models.

Results: We obtain a reasonably high structural similarity with a low enough mean squared error across different human models, demonstrating the robustness and potential for a real-time implementation.

Impact: This study demonstrates the feasibility for AI-assisted real-time calculation of dielectric shimming effect and electromagnetic fields, which could be applied to ultra-high field strengths, where significant inhomogeneity hinders proper evaluation, providing an insightful approach to improve image shading and diagnostics.

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Keywords