Fat-suppressed MRI near metal remains a challenging technical problem. Chemical shift encoded (CSE) water/fat separation enables excellent SNR efficiency compared to short tau inversion recovery (STIR), but the large B0 field offsets and gradients complicate conventional CSE methods near metal. To address these challenges, we propose metal artifact correction and chemical shift encoding using optimized echo spacings, and with deep learning processing for reconstruction of water/fat separated imaging near metal. This method is able to correctly separate water and fat closer to a metallic surgical breast clip than conventional methods, without the drawbacks associated with STIR imaging.
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