Circumventing the curse of dimensionality in magnetic resonance fingerprinting through a deep learning approach

Magnetic resonance fingerprinting (MRF) is a rapidly developing approach for fast quantitative MRI. A typical drawback of dictionary‐based MRF is an explosion of the dictionary size as a function of the number of reconstructed parameters, according to the “curse of dimensionality”, which determines an explosion of resource requirements. Neural networks (NNs) have been proposed as a feasible alternative, but this approach is still in its infancy.