In this study, we explore the possibility of leveraging Deep Learning regularized reconstruction to enable lower flip angle MOLLI acquisition for myocardial T1 mapping. It has been shown in the past that lowering flip angle helps reduce various artifact sources, at the cost of lower signal to noise ratio. Regularized reconstruction can effectively manage image noise as well as increase feature sharpness. This hypothesis has been tested on a group of clinical patients referred for a cardiac MR exam.
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