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Abstract #3451

Deep Learning Based Ultrafast and Robust RF Shimming Using Quadrature B1+ Maps

Xiaoxuan He1, Baolian Yang1, Xucheng Zhu2, Daming Shen1, Eric M. Printz1, Gaohong Wu1, Ling Sun1, and Robert Peters1
1GE HealthCare, Waukesha, WI, United States, 2GE HealthCare, Menlo Park, CA, United States

Synopsis

Keywords: Parallel Transmit & Multiband, Parallel Transmit & Multiband

Motivation: To address B1+ inhomogeneities at ultrahigh fields, RF shimming is commonly used and requires a lengthy per-channel B1+ calibration scan, making it sensitive to motion and potentially affecting image quality.

Goal(s): We aim to develop a faster RF shimming method that predicts RF shims directly from quadrature B1+ maps, bypassing the need for per-channel B1+ maps.

Approach: In-vivo B1+ maps were acquired and used to train a deep neural network, with its performance evaluated in vivo.

Results: Preliminary findings demonstrate the feasibility of deep learning based RF shimming with performance similar to standard RF shimming.

Impact: The deep learning based RF shimming may offer a faster workflow for ultrahigh field imaging, greatly reducing B1+ calibration scan time with less sensitivity to motion and more consistent improvements in image quality.

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Keywords