Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks

Understand

Acquiring images of the same anatomy with multiple different contrasts increases the diversity of diagnostic information available in an MR exam.

• Yet, scan time limitations may prohibit acquisition of certain contrasts, and images for some contrast may be corrupted by noise and artifacts.
• In such cases, the ability to synthesize unacquired or corrupted contrasts from remaining contrasts can improve diagnostic utility.
• For multi-contrast synthesis, current methods learn a nonlinear intensity transformation between the source and target images, either via nonlinear regression or deterministic neural networks.