Fat-Saturated MR Image Synthesis with Acquisition Parameter-Conditioned Image-to-Image Generative Adversarial Network

We trained an image-to-image GAN that incorporates the sequence parameterizations in terms of the acquisition parameters repetition time and echo time into the image synthesis. We trained our model on the generation of synthetic fat-saturated MR knee images from non-fat-saturated MR knee images conditioned on the acquisition parameters, enabling us to synthesize MR images with varying image contrast. Our approach yields an NMSE of 0.11 and PSNR of 23.64, and surpasses the performance of a pix2pix [1] benchmark method. It can potentially be used to synthesize missing/additional MR contrasts and for customized data generation to support AI training.