Figure 2.

Illustration of our framework. The proposed framework consists of a generator (G), which was constructed using a deep convolution network with skip connections, and an image discriminator (D) constructed using a full convolution network. The G transforms the f-DWI into a synthesized apparent diffusion coefficient (s-ADC) using zoomed field-of-view diffusion-weighted imaging (z-ADC) as a reference. The D takes either s-ADC or z-ADC as the input and determines whether the input is a real z-ADC. In addition, to promote G in an effort to retain better features for diagnosis, we introduced a multi-level verification mechanism, including a pre-trained recognition model (C), to extract the multi-level features from the s-ADC and the z-ADC.