Figure 5. Supervised machine learning architectures for semisolid MT/CEST MRF.

a. Quantification of phosphocreatine (pCr) exchange parameters using an artificial NN composed of a single hidden layer.⁷⁰ The input layer was fed with Z-spectrum measurements (analogous to a CEST-MRF schedule where the varied parameter is the saturation pulse frequency offset (ω_rf), and the output was either the pCr proton volume fraction (f_s), exchange rate (k_sw), (B₀), or transmit field (B₁). The NN had 4 variants that were fed with the same input but trained to output each of the 4 different sought-after parameters, using a simulated dictionary. b. Brain semisolid MT exchange parameter quantification and background semisolid MT (Z_ref) contrast image synthesis,⁷³ using a fully connected neural network. The input MRF schedule varied the saturation pulse power (B₁), duration (T_sat), ω_rf, and the recovery time (T_rec). The output included the semisolid MT parameters, and a synthesized MT reference image at 3.5 ppm, calculated by plugging in the resulting parameters and the water T₂-values obtained from a separate protocol in the two-pool BM equations solution. c. Sequential and deep CEST and semisolid MT quantification in the brain.¹⁵ A semisolid MT-oriented MRF acquisition schedule, which varies ω_rf and B₁, yields 30 images that are fed volxelwise into the first NN, together with the quantitative water pool and field homogeneity maps (T₁, T₂, B₀). This NN map the semisolid MT pool exchange parameters, which are then fed, together with the previously obtained quantitative data into the second NN, ultimately yielding the amide proton f_s and k_sw.