Keywords: Liver, Multi-Contrast, Image-to-image translation
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.
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