Keywords: Machine Learning/Artificial Intelligence, Image ReconstructionThe widespread clinical adoption of chemical exchange saturation transfer (CEST) imaging has been hampered by its prolonged scan time, due to multiple data acquisitions over the varying saturation offset frequencies. In this work, we utilize the artifact suppression algorithm and propose an effective deep reconstruction framework with self-calibration mechanisms (DEISM). The DEISM method was validated on brain tumor patients at 3T. In conjunction with deep-learning multi-coil image reconstruction and data-driven artifact suppression mechanisms, DEISM can provide reliable reconstructions of highly accelerated CEST data, yielding superior performance compared to state-of-the-art methods.
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