Learning to synthesize MR contrasts using a self-supervised constrained contrastive learning approach

Motivation: Although deep learning frameworks have been widely used in all aspects of the MR imaging pipeline, the effect of learning tissue-specific information from MR images in improving model performance needs to be understood. Goal(s): We demonstrate the utility of a self-supervised contrastive learning framework that uses multi-contrast information to improve synthesis of T1w and T2w images. Approach: A deep learning model is pretrained to learn T1 and T2 information from a set of multi-parametric MR images. Results: A contrast synthesis framework was developed using few examples of contrast mapping. Embedding relevant contrast information during pretraining synthesized images with improved MSE, SSIM, and PSNR. Impact: Multi-contrast information can be leveraged by self-supervised deep learning models to understand underlying tissue characteristics and synthesize new MR contrast-weighted images. This demonstrates the wider applicability of embedding tissue-specific information in improving different aspects of the MR imaging pipeline.