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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