Keywords: Contrast Mechanisms, MR Fingerprinting, relaxometry reconstruction, B0 estimation, self-supervised deep learningDeep neural networks are increasingly employed in MRF reconstruction recently, as an alternative for conventional dictionary matching. However, most previous deep leanring MRF networks were trained based on the dictionary pairs (generated using theoretical bloch equation) or in vivo acquisitions paired with dictionary matching reconstructions (as training labels), whose reconstruction accuracy relies heavily on the dictionary construction and the number of sampling arms. In this work, we propose a self-supversied deep learning MRF method, namely MRF-Mixer, which can result in more accurate MRF reconstructions compared with dictionary matching.
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