Figure 5
Schematic diagram of the two-stage approach used for making the conversion model. In the first stage, the generator (GC→N), which generates synthetic NECT from real CECT, is trained adversarially using a conditional generative adversarial network. In the second stage, another generator (GN→C) that generates synthetic CECT from NECT is trained using a deep convolutional neural network. During the second stage of training, synthetic NECT, which is generated from and perfectly aligned with real CECT, is used as input data, resolving the misregistration issue between input data and ground truth (real CECT). NECT, nonenhanced CT; CECT, contrast-enhanced CT; LAdv, adversarial loss; Lrec, reconstruction loss; GC→N, generator that generates synthetic NECT from real CECT; GN→C, generator that generates synthetic CECT from NECT.
