Multi-Contrast Low-field MR Image Enhancement via Self-supervision

Motivation: Restoring the structure that is barely visible on the MR images is a major challenge for self supervised enhancement using one input, especially in low-field MR imaging applications. Goal(s): To improve the image quality and the visibility of some clinically relevant structure in certain contrast in MR imagesApproach: We proposed a self-supervised learning framework using the shareable information from other image contrasts. More specifically, two mutual modulations with a cyclic consistency constraint are introduced to guide the training. Results: Preliminary results on 0.25T spine MR images suggest that our method can achieve superior results compared to other self-supervised methods. Impact: The work shows the feasibility of adopting the multiple contrast information to improve the MR images with poor quality without acquiring low resolution/high resolution pairs. It leads to more accurate diagnoses.