Skip to main content
. Author manuscript; available in PMC: 2024 May 1.
Published in final edited form as: Magn Reson Med. 2022 Dec 31;89(5):1901–1914. doi: 10.1002/mrm.29574

FIGURE 1.

FIGURE 1

Generative adversarial network (GAN)-saturation transfer (ST) architecture. (A) A conditional GAN framework, receives N raw, molecular information encoding semi-solid magnetization transfer/chemical exchange saturation transfer images, and is trained to simultaneously output the quantitative proton volume fraction and the exchange rate maps. (B) A fully connected neural network, receiving the full-length raw magnetic resonance fingerprinting image series (M > N) pixelwise, as well as T1, T2, and B0 maps, and yielding the reference proton volume fraction and exchange rate maps.34 The output of this network was used for training GAN-ST.