In MR fingerprinting, quantitative maps are obtained by matching the measured signal to a pre-computed dictionary. However, a key constraint of dictionary matching is the exponential growth of the dictionary with the number of parameters. A deep learning method named DRONE overcomes this constraint by using deep learning to map the magnitude-valued signal to the underlying tissue parameters. Here we describe an extension of DRONE that jointly estimates a phase term to enable mapping complex-valued signals and improve the quantitative accuracy. We test the accuracy in the ISMRM NIST phantom and demonstrate the clinical utility in patients with brain metastases.
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