The purpose of this work was to develop and evaluate a novel deep learning-based framework termed Reference-free Latent map-eXtracting MANTIS (Relax-MANTIS) for efficient MR parameter mapping. Our approach incorporated end-to-end CNN mapping, the concept of cyclic loss to enforce data fidelity and without the need of explicit training references. Our results demonstrated that the proposed framework produced accurate and robust T1 mapping in knee and low-SNR lung UTE MRI. The good quantitative agreement to the reference method suggests that Relax-MANTIS allows potentially accelerated quantitative mapping without modifications of scan protocol and sequence for high-resolution knee and whole lung T1 quantification.
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