Synthesizing multiple realistic MR phase images using a multi-modal generative model

Motivation: Deep learning MRI reconstruction methods face challenges in available datasets to train models. Clinical scans can be a source for diverse data but a challenge is obtaining MRI phase. Goal(s): We propose a method to generate multiple plausible synthetic phase images from a single magnitude-only input. Approach: We train a multi-modal generative model enforcing consistency in the latent space during training. We evaluate the effect of latent vector dimension on diversity and quality of the synthetic images with FID score and training image reconstruction models with this synthetic data. Results: Higher latent vector dimension resulted in more diverse and higher quality synthetic images. Impact: This method could be used to generate multiple plausible phase images from a single scan to model effects of varying field homogeneity, RF coils, echo time, motion, flow, and susceptibility