Synthetic derivative T2-weighted abdominal images from T1-weighted images using a generative adversarial network (GAN)

Abstract

Motivation: Either fast 2D T2-weighted abdominal imaging or 3D T2 MIP techniques have limitations. There remains a need for fast 3D T2 abdominal high-resolution imaging. Goal(s): To develop a conditional GAN model to synthesize T2-weighted images from 3D high-resolution T1-weighted abdominal images preserving spatial resolution of the source images. Approach: Abdominal images acquired from 39 volunteers were included for the study. A conditional GAN model was trained to generate T2-weighted images from T1-weighted images slice by slice. Results: Overall, the generated T2-weighted images were similar to the real T2-weighted images, though some contrast differences in the bowels and kidneys were seen. Impact: This proof of principle study shows the GAN model can be used to generate T2-weighted images from T1-weighted images, with the potential for rendering high quality volumetric 3D high-resolution abdominal T2-weighted images that is superior to current 3D MIP methods.