FIGURE 2.

The neural network architecture used in this study. The 13 complex-valued coefficients of each voxel are concatenated and fed into the fully connected network. Skip connections⁴³ are incorporated to avoid the vanishing gradient problem during training. The network outputs the underlying tissue parameters $m_0^s$, T₁ and $T_2^f$, but additional parameters, such as B₀ and B₁, and be added to the output layer (cf. Supporting Information Figure S4).