While versatile soft tissue contrasts are achievable in MRI, contrast attainable from each scan is predetermined by the imaging protocol. A retrospective tuning of contrast will provide an opportunity to normalize MRI data for radiomics analysis. In this study, we present a new paradigm to obtain a spectrum of contrasts from a single T1-weighted image. Using deep learning, T1 map, proton density map, and B1 map are predicted from every T1-weighted image, and new contrasts can be obtained with the application of Bloch equations. The method has been validated in knee MRI with high accuracy achieved.
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