At high field, tailored static or, better, dynamic RF shimming can be used to reduce artifacts due to transmit B1 field inhomogeneity, but those methods require extra time for calibration, which can disrupt clinical workflows. Recently, universal pulses (UP) were introduced in brain imaging to get rid of calibration. In this work, a machine learning method is proposed to extend universal pulse kT-point design to body imaging where inter-subject variability is more pronounced, by classifying subjects into one of several predefined categories. This method outperforms UP design, and yields images similar to those obtained with state-of-the-art tailored design.
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