MR Fingerprinting enables the quantification of multiple tissue properties from a single, time-efficient scan. Here we present a novel Diffusion Tensor MR Fingerprinting acquisition scheme that is simultaneously sensitive to T1, T2 and the full diffusion tensor. We circumvent the long-standing issue of phase errors in diffusion encoding and expensive dictionary matching by using a neural network architecture capable of learning the non-linear relation between fingerprints and multiparametric maps, robustly mitigating motion, undersampling and phase artifacts. As such, our framework enables the simultaneous quantification of relaxation parameters together with the diffusion tensor from a single, highly accelerated acquisition.
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