Fig. 6. A demonstration of two MRI quantification strategies/architectures.

a Deep learning reconstruction of quantitative magnetic resonance fingerprinting (MRF) information. A fully connected neural network is trained using simulated signal trajectories. During inference, it receives a series of raw MRF images pixel-wise (gray-scale images, left), as well as auxiliary maps (color, top left), yielding quantitative parameter maps (top right). b A further acceleration in quantitative MRI scan time can be achieved by training a generative adversarial network (GAN) using a smaller subset of raw input data to yield the same quantitative output maps. Reproduced and modified from Weigand-Whittier et al. [199]