A physics-based deep learning reconstruction is proposed for cardiac MRF that is based on the deep image prior framework, which employs self-supervised training and does not require additional in vivo training data. Calculation of the forward model is accelerated using a neural network to rapidly output MRF signal timecourses (in place of a Bloch simulation) and low-rank modeling to reduce the number of NUFFT operations. The proposed reconstruction enables cardiac T1/T2 mapping during a shortened 5-heartbeat breathhold and 150ms acquisition window.
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