Keywords: AI/ML Image Reconstruction, Low-Field MRI
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 images
Approach: 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.
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