Fig. 5.

Schematic of the spatially-constrained tissue quantification (SCQ) network. First, a measured signal evolution is input to a feature extraction subnetwork. The network consists of 4 fully-connected layers, and it outputs a low-dimensional feature vector. This calculation is repeated for each pixel to generate feature maps, which have a lower dimension than the undersampled MRF image series. Next, a block of pixels is input to the spatially constrained quantification subnetwork. This subnetwork is implemented with a U-net, and it uses blocks of data from the feature maps to estimate T₁ or T₂ at a target pixel. Note that a separate SCQ network is trained for each tissue property.