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. Author manuscript; available in PMC: 2021 Feb 1.
Published in final edited form as: Proc SPIE Int Soc Opt Eng. 2020 Feb 28;11317:113170U. doi: 10.1117/12.2549318

Figure 1.

Figure 1.

Basic modules of the GAN-CIRCLE used for HR CT image reconstruction. Here, X is the set of low-resolution CT scans, and Y is its high-resolution counterparts. The network consists of two basic GAN modules (Generator: G, Discriminator: DY) and (Generator: F, Discriminator: DX), which are responsible for low-to-high and high-to-low resolution image reconstruction, respectively. Different loss functions are synergistically coupled to train the network, while constrained under the regularization terms related to cycle-consistency and identity loss to avoid overfitting.