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Magnetic resonance imaging (MRI) offers superior soft tissue contrast and iswidely used in biomedicine. However, conventional MRI is not quantitative,which presents a bottleneck in image analysis and digital healthcare.Typically, additional scans are required to disentangle the effect of multipleparameters of MR and extract quantitative tissue properties. Here weinvestigate a data-driven strategy Q^2 MRI (Qualitative and Quantitative MRI)to derive quantitative parametric maps from standard MR images withoutadditional data acquisition. By taking advantage of the interdependency betweenvarious MRI parametric maps buried in training data, the proposed deep learningstrategy enables accurate prediction of tissue relaxation properties as well asother biophysical and biochemical characteristics from a single or a few imageswith conventional T_1/T_2 weighting. Superior performance has been achieved inquantitative MR imaging of the knee and liver. Q^2 MRI promises to provide apowerful tool for a variety of biomedical applications and facilitate the nextgeneration of digital medicine.

Quantitative Parametric Mapping of Tissues Properties from Standard Magnetic Resonance Imaging Enabled by Deep Learning